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No Priors Ep. 128 | With DeepLearning.AI Founder Andrew Ng

发布时间 2025-08-21 10:00:47    来源
吴恩达 (Andrew Ng)，人工智能革命中的杰出人物，做客 “No Pires” 播客，讨论了人工智能的现状和未来发展方向，特别关注了 “具身人工智能 (agentic AI)” 的概念。吴恩达强调，人工智能能力的进步将来自多个维度，包括具身工作流、多模态模型和新技术，而不仅仅是通过大型公司通过大量公关宣传所强调的模型规模化。 吴恩达提出了 “具身人工智能 (agentic AI)” 这个术语，以避免关于什么是“代理”的争论，而是专注于构建具有不同程度自主性的系统。虽然围绕具身人工智能的营销炒作迅速加速，但实际的业务进展也在增长，尽管没有营销宣传所暗示的那么快。实施真正的 AI 代理的最大障碍不是技术，而是人才。他强调在构建有效的代理时，系统性的错误分析和评估至关重要，而这往往是经验不足的团队所缺乏的技能。 吴恩达认为，通过具身工作流可以自动化大量工作，但他强调，构建此类工作流需要通常锁在人们头脑中的外部知识。目前，人类工程师和产品经理在做出上下文相关的决策方面至关重要，而这些决策是 AI 代理目前还无法复制的，特别是在处理专有或非通用知识时。 在吴恩达看来，代理的最强例子是 AI 编码代理。他强调编码代理是产生经济价值的两个明确类别之一，另一个是回答人们的问题。他认为这些代理在规划和执行多步骤流程以构建软件方面具有高度自主性。他相信编码的经济价值推动了大量资源投入到构建有效的编码代理中。吴恩达更喜欢“AI辅助编码”而非“感觉编码 (vibe coding)” 这个说法，因为后者暗示任务比实际情况更简单。使用 AI 辅助使编码成为一项深刻的智力活动。 吴恩达指出，AI 辅助编码正在通过加速编码速度和降低成本来改变创业公司的性质。这会将瓶颈转移到产品管理，要求产品经理依赖直觉和客户共情。他对尝试自动化产品管理某些方面的工具，例如使用 AI 代理进行市场调研，持谨慎乐观态度。但他仍然认为，在取代人类产品经理方面，它们不如编码工具有效。 吴恩达认为，对 AI 技术有深刻理解的创始人更有可能在当前快速发展的环境中取得成功。创始人应该掌握新兴的 AI 技术，否则他们很难领导公司。以技术为导向的产品领导者比以业务为导向的领导者更有可能成功。他强调，对于那些想要改变世界的人来说，努力工作和挑战现状至关重要。 他观察到，创始人主要有两种类型：一种痴迷于他们的企业获胜，另一种痴迷于他们的客户获胜。吴恩达更看重以客户为中心。他强调需要根据对客户的深刻理解做出果断且快速的决策。 吴恩达还谈到了由于人工智能的快速发展，他的观点和工作流程是如何变化的。在招聘工程师时，他非常重视 AI 技能。软件工程师是其他学科的先驱，因为软件工程领域的工具更加先进。此外，他认为未来的工作性质可能涉及更小、高度熟练的团队，在 AI 的辅助下，其表现优于更大、成本更低的团队。 吴恩达相信，在未来五年内，由于 AI 的集成，人们将更加有力量和能力。拥抱 AI 的个人将比大多数人意识到的更加高效和有能力。
Andrew Ng, a prominent figure in the AI revolution, joined the "No Pires" podcast to discuss the current state and future direction of AI, particularly focusing on the concept of "agentic AI." Ng emphasizes that progress in AI capabilities will come from multiple vectors, including agentic workflows, multi-modal models, and new technologies, and not solely from scaling models as heavily emphasized by large companies with significant PR influence. Ng introduced the term "agentic AI" to move away from debates about what constitutes an agent and instead focus on building systems with varying degrees of autonomy. While marketing hype around agentic AI has accelerated rapidly, real business progress is also growing, albeit not as quickly as the marketing suggests. The biggest obstacle to implementing true AI agents is not technology but talent. He emphasizes the importance of systematic error analysis and evaluation in building effective agents, a skill often lacking in less experienced teams. Ng believes that a significant amount of work can be automated through agentic workflows but emphasizes that building such workflows requires external knowledge often locked in the heads of people. Currently, human engineers and product managers are crucial in making context-aware decisions that AI agents cannot yet replicate, particularly when dealing with proprietary or non-general knowledge. According to Ng, the strongest example of agency is AI coding agents. He highlights coding agents as one of the two clear buckets of economic value that include answering people's questions. He finds these agents highly autonomous in planning and executing multi-step processes to build software. He believes the economic value of coding has driven significant resources towards building effective coding agents. Ng prefers the term "AI-assisted coding" over "vibe coding" because the latter implies the task is simpler than it is. Using AI assistance makes coding a deeply intellectual exercise. Ng notes that AI-assisted coding is changing the nature of startups by accelerating coding speed and reducing costs. This shifts the bottleneck to product management, requiring product managers to rely on gut instinct and customer empathy. He is cautiously optimistic about tools that attempt to automate aspects of product management, such as market research using AI agents. Still, he believes they are not yet as effective as coding tools in replacing human product managers. Ng argues that founders with a strong understanding of AI technology are more likely to succeed in the current rapidly evolving landscape. Founders should be on top of emerging AI technologies, or they struggle to lead the company. The technology-oriented product leaders are more likely to succeed than business-oriented leaders. He emphasizes the importance of hard work and a willingness to challenge the status quo for those seeking to change the world. He observes that there are two main types of founders: those obsessed with their business winning and those obsessed with their customers winning. Ng values customer obsession. He emphasizes the need for decisiveness and rapid decision-making based on a deep understanding of the customer. Ng also addresses how his views and workflows are changing due to AI's rapid progress. When hiring engineers, he places a high value on AI skill set. Software engineers are the harbinger for other disciplines because tools are more advanced in software engineering. Furthermore, he suggests that the future nature of work might involve smaller, highly skilled teams with AI assistance outperforming larger, lower-cost teams. Ng believes that in the next five years, people will be more empowered and capable due to AI integration. Individuals embracing AI will be significantly more productive and capable than most realize.

摘要

Andrew Ng has always been at the bleeding edge of fast-evolving AI technologies, founding companies and projects like Google ...

GPT-4正在为你翻译摘要中......

中英文字稿  

Hi listeners, welcome back to No Pires. Today, a lot of nyer here with Andrew Aing. Andrew is one of the godfathers of the AI revolution. He was the co-founder of Google Brain, Coursera, and the Venture Studio AI Fund. More recently, he coined the term agentic AI and joined the board of Amazon. Also, he was one of the very first people a decade ago to convince me that deep learning was the future. Welcome, Andrew. Andrew, thank you so much for being with us. No, always great to see you.
嗨，听众们，欢迎回到No Pires。今天，有很多新人和Andrew Aing在这里。Andrew是人工智能革命的奠基者之一。他是Google Brain、Coursera以及创业工作室AI Fund的共同创始人。最近，他提出了“代理型人工智能”这个术语，并加入了亚马逊的董事会。十年前，他就是最早让我相信深度学习是未来的人之一。欢迎你，Andrew。Andrew，非常感谢你来参加我们的节目。 不用谢，总是很高兴见到你。

I'm not sure we should begin because you have such a broad view of these topics, but I feel like we should start with the biggest question, which is, if you look forward at capability growth from here, where does it come from? It's come from more scales, come from data work. Multiple vectors of progress. So I think there is probably a little bit more crucial that the scalability limit is to be smooth, so hopefully you can see me, because there is a really, really difficult. Societies, perception of AI has been very skewed by the PR machinery of a handful of companies with amazing PR capabilities.
我不太确定我们是否应该开始讨论，因为你对这些话题的看法非常广泛。但我觉得我们应该从最大的问题开始，那就是：从现在开始展望能力的增长，增长的源头在哪里？能力增长可能来自于更大规模的数据和更复杂的数据处理，多方面的进步。我认为更重要的可能是确保扩展性的限制能够被平滑处理，这样希望你能理解我的观点，因为这是一个非常困难的问题。社会对人工智能的看法很大程度上受到少数几家公司强大公关宣传的影响，这种看法实际上非常偏颇。

And because that number of companies drove scales in narrative, people think of scale first of this vector progress. But I think, you know, agentic workflows, the way we build multi-model models, we have a lot of work to build concrete applications. Multiple vectors of progress, as well as wild cards like brand new technologies like Confusion models, which are used to generate images for the most part, will that also work for generating text? I think that's exciting. So I think that would be multiple ways for AI to make progress.
由于有大量公司推动了叙事规模的发展，人们首先想到的就是这种规模的进展。但我认为，自主工作流程和我们构建多模型模型的方式，需要很多工作来构建具体的应用程序。进展的多种维度，以及一些不可预测的因素，如新的技术如主要用于生成图像的Confusion模型，这些技术是否也能用于生成文本呢？我认为这是令人兴奋的。因此，我认为AI有多种方法可以取得进展。

You actually came up with the term agentic AI. What did you mean then? So when I decided to start top of agentic AI, which wasn't a thing when I started using the term, my team was slightly annoyed at me. One of my team members that were named, I said, Andrew, the world does not need you to make up another term, but I decided to do it anyway and for weather reasons to stop. And the reason I started to top of agentic AI was because a couple years ago, I saw people were spending a lot of time debating, is this an agent? Is this not an agent? What is an agent?
你实际上想出了“主动型AI”这个词。当时你是什么意思？当我决定开始使用“主动型AI”这个词时，这个概念还不存在，我的团队对此有些不满。其中一个团队成员，我喊他安德鲁，他说：这个世界不需要你再创造一个新词。然而，我还是决定这么做，并且没有理由停下来。我之所以提出“主动型AI”，是因为几年前我看到大家花费大量时间争论：这是代理吗？这不是代理吗？到底什么是代理？

And I felt there was a lot of good work and there was a spectrum of degrees of agency, whether it's highly autonomous agents that could plan, take multiple sets of using to a lot of stuff by themselves. And then things that were lower degrees of agency, where we're prone to now, we're affecting this output. And I felt like rather than debating, this is an agent or not, let's just say the degrees of agency and say it's all agentic, so you spend your time actually building this. So I started to push the term agentic AI.
我感到有很多好的研究成果，并且存在不同程度的自主性。无论是那些可以自主规划并执行多个任务的高度自主的代理，还是那些自主性较低的，更容易受到外部影响的系统。我觉得与其争论某个系统是否是代理，不如用“自主性程度”来描述，认为所有系统都有某种程度的自主性，这样可以将精力放在实际的构建上。因此，我开始推广“自主智能体”这个术语。

What I did not expect was that several months later, eventually marketers, we get a hold of this term and use this as a sticker to stick about everything in sight. And so I think the term agentic AI really took off. I feel that the marketing hype has gone like that insanely fast. But the real business progress has also been rapidly growing, but maybe not as fast as the marketing.
我没有预料到的是，几个月之后，市场推广人员竟然抓住了这个词，把它贴在所有能想到的东西上。因此，我觉得“代理型人工智能”这个词真的变得非常流行。我觉得市场的炒作实在是发展得太快了。不过，实际业务的进展也在快速增长，但可能没有市场炒作那么快。

But do you think of the biggest obstacles right now to true agents actually being implemented as AI applications? Because to your point, I think we've been talking about it for a little while now. There are certain things that we're missing initially that are now in place in terms of everything from certain forms of inference time compute on through to forms of memory and other things that allow you to maintain some sort of state against what you're doing. What do you view are the things that are still missing or need to get built or most sort of foment progress on that end?
你认为目前阻碍真正的智能代理作为人工智能应用实现的最大障碍是什么？因为正如你所说，我们已经谈论这个话题有一段时间了。最初我们缺少的一些东西，现在已经具备了，比如某些形式的推理计算、记忆形式和其他可以帮助你在操作过程中保持状态的东西。你认为哪些东西仍然缺失，或者需要构建，或者最需要推动这方面的进展？

I think the technology component level, this stuff that I hope for example computer use kind of works often doesn't work. I think so the God rails e-vows is a huge problem. How do you quickly evaluate these things and drive e-vows? So I think the component is this room for improvement. But what I see is the single biggest barrier to getting more agentic AI workflows implemented is it's actually talent. So when I look at the way many teams built agents, the single biggest differentiator that I see in the market is does the team know how they drive a systematic error analysis process with e-vows?
我认为在技术层面上，希望计算机使用的技术能够正常运作，但往往并不是这样。我认为技术方面的问题很大是评估系统。如何快速评估这些问题并进行改进？所以我觉得这方面还有提升的空间。但我看到，实现更多自主型AI工作流程的最大障碍其实是人才问题。当我观察许多团队建设智能代理的方式时，我发现市场上的最大区别在于团队是否知道如何通过系统化的错误分析过程进行改进。

So you're building the agents by analyzing at any moment in time what's working, what's not working, what the improve as supposed to less experience seems kind of try things in a more random way than it's just six long time. And what I look at was huge range of business small and large. It feels like there's so much work that can be automated through agentic workflows, but you know, detailing the skills and maybe the software tooling. I don't know. Just isn't there to drive that disciplined entry and process to get the stuff how much of that engine process could you imagine being automated with AI? You know, it turns out that a lot of this process of building agentic workflows requires ingesting external knowledge, which is often locked up in the heads of people.
所以你是在通过分析每时每刻哪些有效、哪些无效以及需要改进的地方来构建代理，而不是像经验较少的人那样随机尝试，这样会花费很长时间。我观察到大小企业都有一个巨大的领域可以通过代理工作流实现自动化，但可能细节的技巧和软件工具还不够完善，无法推动这种有纪律的进入和处理过程。你能想象有多少这样的流程可以通过人工智能实现自动化吗？事实证明，构建代理工作流的许多过程都需要吸收外部知识，而这些知识往往是藏在人们的脑海中的。

So until and unless we built, you know, AI avatars can interview employees doing the work and that's a visual AI that can look at the computer monitor. I think maybe eventually, you know, but I think at least right now for the next year or two, I think there's a lot of work for human engineers to do to build agentic workflows. What's more, the kind of collection of data feedback, etc. for certain moves that people are doing is that other things that I'm sort of curious like what that translates into tangibly versus. Yeah, so we have one example.
所以，直到我们开发出可以面试员工的AI化身，也就是能够观察电脑屏幕的可视化AI为止，我认为在未来一年或两年内，人类工程师仍然有很多工作要做，以建立智能化的工作流程。另外，对于人们的一些操作行为，我们需要收集数据反馈等信息，我很好奇这些信息最终能转化成什么实际的成果。我们有一个例子。

So I see a lot of workflows like, you know, maybe I customise your document, you're going to convert the document to text, then maybe do a web search for some compliance reason to see you're working a vendor you're not supposed to and then look at the database records, see the pricing, right, save it somewhere else in some ways. There's multi-state agentic workflows kind of mixed-gen robotic process automation. So the implementers that it doesn't work, you know, is it a problem if you got to invoice date wrong? Is that a problem or not? Or if you route to the message, the wrong person for verification.
我看到很多工作流程，比如说，我可能会定制你的文档，然后你可能需要把文档转换成文本，再进行一次网络搜索，以确定在合规方面你是否与不该合作的供应商合作。接着再查看数据库记录，看看价格，然后以某种方式把信息保存在其他地方。这些是多步骤的自动化工作流程，类似于混合型的机器人流程自动化。那么执行者觉得，这样的流程如果出了问题，比如发票日期弄错了，或者信息核实时发给了错误的人，这算不算是个问题呢？

So when all of we implement this thing, you know, almost always it doesn't work the first time, but into know what's important for your business process and is it okay that I don't know, I bothered the CEO of the company too many times or is the CEO of me if it doesn't mind, verifying some invoices. So all that external contextual knowledge, often at least right now, I see thoughtful human product managers or human engineers having to just think through this and make these decisions. So can an AI agent do that someday? I don't know. It seems pretty difficult right now, maybe someday.
所以，当我们所有人一起实施这个项目时，你知道，几乎总是第一次不会成功。但是了解对你的业务流程重要的是什么，这一点很重要。比如说，我不清楚，但我去麻烦公司的CEO太多次，这样可以吗？或者说，如果CEO被要求核实一些发票的话，他是否介意？所有这些外部的背景知识，至少目前，我看到谨慎的人类产品经理或工程师需要亲自思考并做出这些决定。那么，某天AI代理能做到这点吗？我不清楚。现在来看，这似乎还是相当困难的，或许未来某天能实现。

But it's not in the internet pre-training data set and it's not in a manual that we can automatically extract. I feel like for a lot of work to be done building agentic workflows, that data set is proprietary. It's just not it's not general knowledge on the internet. So figuring it out, it's still exciting work to do. What is the, if you just look at this spectrum of agentic AI, what's the strongest example of agency you've seen? I feel like leading edge of agentic AI, I've been really impressed by some of the AI coding agents.
但它不在互联网上的预训练数据集中，也不在我们可以自动提取的手册中。我觉得，为了构建自主工作流程，很多工作需要利用专有数据集。这些数据集并不是互联网上的一般常识。所以，要弄清楚这些东西，依然是令人兴奋的工作。如果你看看自主人工智能这个领域，你见过的最强的自主能力的例子是什么？我觉得在自主人工智能的前沿，我对一些AI编程代理印象深刻。

So I think in terms of economic value, I feel like the two very clear, very apparent buckets. One is answering people's questions. Probably, opening AI chat, you can see the mockery leader of that with Rolex, take off lift off velocity. The second massive bucket of economic value is coding agents, where coding agents like my personal favorite club, De La Toe, right now is club code. Maybe we'll change at some point, but I just use it. Love it. Heidi Autonomous in terms of planning out what to do to build a software, building a checklist, going through one of the times, so there's ability to plan a multi-step thing, execute the multi-step plan.
我认为在经济价值方面，有两个非常明显的领域。第一个是回答人们的问题。像是使用AI聊天工具，您可以看到其飞速发展的趋势，这就像是劳力士的领军地位，快速起飞。第二个巨大经济价值领域是代码代理，我个人特别喜欢这个俱乐部，目前我最喜欢的俱乐部就是代码俱乐部。也许将来会有所改变，但我现在就是很喜欢用它。它在规划如何构建软件、制定清单、一步一步地执行这些步骤方面表现得很出色，能够规划和执行多步骤的任务。

Is one of the most highly autonomous agents out there being used that actually works. There's other stuff that I think doesn't work. Something computer used to go shop for something from me and browse online. Some of those things are really nice demos, but not yet production. Because I was sort of criteria in terms of what needs to be done and more variability around actions, or do you think there's a better training set or sort of set of outputs for coding? I'm sort of curious why does one work so while there almost feels magical at times, and the others are really struggling as use cases so far.
这是目前使用的最高度自主的代理之一，而且它确实有效。还有其他一些我认为不起作用的东西。比如，一些计算机程序用于帮我购物或者浏览网页。有些东西是很不错的演示，但还没有达到实用的阶段。因为我对于需要做的事情和动作的多样性有一些标准，或者你认为有更好的训练集或者编码输出集吗？我很好奇为什么这个可以成功地运作，有时甚至让人觉得神奇，而其他的用例到目前为止却举步维艰。

I think, you know, engineers really like getting all sorts of stuff to work, but the economic value of coding is just clear and apparent and massive. So I think the sheer amount of resources dedicated to this has led to a lot of smart people for whom they themselves are the user, so also good instinct-long product, building really amazing coding agents. And then I think I don't know. You don't think it's a fundamental research challenge. You think it's like capitalism at work, and then domain knowledge in a lab.
我认为，你知道，工程师们确实喜欢让各种东西运作起来，但编程的经济价值是显而易见且巨大的。所以我觉得大量资源的投入吸引了许多聪明人参与进来，他们本身也是用户，因此他们有良好的直觉来创建出长期有用的产品，打造出非常出色的编程工具。然后我觉得，我不太确定。你不认为这是一个基础研究的挑战。你认为这更像是资本主义在发挥作用，以及实验室中的领域知识。

Oh, I think capitalism is great at solving fundamental research problems. Yeah. At what point do you think models will effectively be bootstrapping themselves in terms of, you know, 90% of the code of a model will be written by agent-coding agents. For the error analysis, I feel like where I started to think was slowly getting there. So some of the leading foundation model companies are clearly well-deserved publicly. They're using AI to rally all the codes. One thing I find exciting is AI models using agent-to-work flows to generate data for the next generation of models. So I think the Lama research we were talking about is what older virtual Lama would be used to think for a long time to generate puzzles. Then you train the next generation of the model to try to solve really quickly without me to think as long. So I find that exciting too.
哦，我觉得资本主义在解决基础研究问题方面很有一套。是的。你觉得在什么阶段，模型在某种意义上能够自我完善，比如说，模型的90%代码是由代理编码代理编写的。我感觉在错误分析这方面，我们已经开始慢慢接近这个目标。一些领先的基础模型公司显然在公众中享有应得的声誉。他们正在利用人工智能来集合所有的代码。我觉得很令人兴奋的一点是，人工智能模型使用代理工作流来为下一代模型生成数据。我认为我们之前谈论的Lama研究就是这样的：年长的虚拟Lama会花很长时间来思考生成难题，然后训练下一代模型快速解决这些问题，而不需要花费那么长时间去思考。我也觉得这很让人兴奋。

Yeah, multiple vectors of progress. It feels like AI is not just one way to make progress. There's so many spy people pushing forward in so many different ways. I think you have rejected the term vibe coding in favor of AI-assisted coding. What's the difference? You know, I know that you do the latter. You're not vibing.
是的，有多种进步途径。我感觉人工智能的进步不仅仅局限于单一方向，有许多聪明的人在各个方面推动它的发展。我想你已经不再使用“氛围编码”这个词，而更倾向于“AI辅助编码”。这两者有什么不同呢？你知道的，我了解你是做后者的，不是做“氛围”那一套的。

Yeah. Vive coding leads people to think, you know, like, I'm just going to go to the vibes and set all the changes, it creates a suggestion or whatever. And it's fine that sometimes you could do that and it works, but I wish it was that easy. So when I'm coding for a day or for an afternoon, I'm not like going with the vibes. It's like a deeply intellectual exercise. And the thing that the term vibe coding makes people think is easier than it is. So frankly, after a day of using AI-assisted coding, I'm exhausted mentally, right? So I think of it as rapid engineering, where AI is letting us build serious systems, build products, much faster than ever before. But it is, you know, engineering just done really rapidly.
是的。大家常常把 "vibe coding" 想得太过简单，觉得只要随心所欲地去写代码，做一些调整，提出一些建议就行了。有时候这种方式确实能带来不错的结果，但我希望一切能真那么简单。当我花一天或一下午的时间写代码时，我并不是随便应付，而是在进行一场深刻的智力活动。"vibe coding" 这个说法让人误以为编程比实际简单。不过，老实说，经过一天使用 AI 辅助编程后，我的脑子真的很累。我认为这更像是快速工程，AI 让我们能够比以往更快地构建严肃的系统和产品，但本质上这仍然是工程，只是速度更快而已。

Do you think that's changing the nature of startups? How many people you need? How you build things? How you approach things? Or do you think it's still the same old kind of approach, but you just have people to get more leverage because they have these tools now? So, you know, yeah, I find that we've built startups and it's really exciting to see how rapid engineering, AI-assisted coding, is changing the way we build startups. So there's so many things that, you know, would have taken a team of six engineers, like three months to build. They're now today, one of my friends are I, which is building a weekend.
你认为这改变了初创公司的性质吗？需要多少人？如何建立公司？如何处理事情？还是说，还是老一套的方法，只是因为有了这些工具，人们获得了更多的助力？我觉得我们正在创建初创公司，很高兴看到快速工程和AI辅助编程是如何改变我们创建初创公司的方式。有很多事情，以前可能需要六名工程师花三个月才能完成，而现在，我的一个朋友和我可以在一个周末内完成。

And the fascinating thing I'm seeing is, if we think about building a startup, the core loop of what we do, right? I want to build a product that uses love. So the core iteration loop is right software, you know, it's a software engineering work and then the product managers maybe go to user testing, look at it, go back, got whatever to decide how to improve the product. So when we go look at this loop, the speed of coding is accelerating, the cost is falling. And so increasingly, the bottleneck is actually product management.
我看到的一个有趣现象是，在我们考虑创建一家初创公司的过程中，有一个核心流程。我们的目标是打造一款充满热爱的产品。因此，这个核心迭代循环包括编写软件、进行软件工程工作，然后产品经理可能会进行用户测试、查看产品、返回并决定如何改进产品。当我们观察这个循环时，会发现编码的速度在加快，成本在降低。所以，实际上现在的瓶颈反而是产品管理。

So the product management bottleneck is now looking at build, what do we want? Much faster. Well, the bottleneck is deciding what do we actually want to build at privacy. If it took you, say three-week-to-builder prototype, if you need a week to get user feedback, it's fine. But if you can now build a product in the day, then boy, if you have to wait a week for user feedback, that's really painful. So I find my teams, frankly, increasingly relying on gut because we're going to collect a lot of data that informs our very human mental model, our brains, mental model, whether user ones.
所以现在产品管理的瓶颈是在构建阶段。我们希望能快很多。瓶颈是决定我们实际想要构建什么以保障隐私。如果之前需要三周时间去开发一个原型，再花一周时间获取用户反馈，这还可以。但如果现在你能在一天内完成产品开发，而还要等上一周才能得到用户反馈，那就很让人痛苦。因此，我发现我的团队越来越依赖直觉，因为我们要收集大量数据来完善我们大脑中关于用户需求的人为心理模型。

And then we often, you know, have to have deep customer empathy. So you can just make products decisions like that really, really fast in all the drive progress. Have you seen anything that actually automates some aspects of that? I know that there have been some versions of things, or people, for example, are trying to generate market research by having a series of bots kind of react in real time. And that almost forms your market or your user base as a simulated environment of users. Have you seen any tooling that work or take off, or do you think that's coming, or do you think that's too hard to do?
然后，我们通常需要有深刻的客户同理心，这样一来，你可以非常快速地做出产品决策，并推动进展。你有见过任何能自动化其中某些方面的东西吗？我知道有一些版本的尝试，比如说，通过让一系列机器人实时反应来生成市场研究，这几乎就形成了一个模拟用户环境，类似于你的市场或用户群。你有没有见过哪些工具已经有效或普及？还是你认为这种工具还未出现，或是认为这太难实现了？

Yeah, so there's a bunch of tools to try to speed up product management. I feel like, well, the recent fake model IPO is one, you know, really that book design, AI, hiding it, you know, doing to the great job. Then there are these tools that I try to use AI to hold interview prospective users. And as you say, we looked at some of their scientific papers on using a flock of AI agents to simulate, you know, a group of users and how to calibrate that. It all feels promising and early and hopefully, while they're exciting in the future, I don't think those tools are set everything product managers, nearly as much as coding tools are set everything software engineers.
是的，有很多工具可以加速产品管理。我觉得最近的一个例子就是所谓的模型IPO，确实在书籍设计和AI隐藏方面做得很好。还有一些工具尝试用AI来采访潜在用户。正如你所说，我们查看了一些他们的科学论文，研究使用一群AI代理来模拟用户群体以及如何校准这些工具。虽然这一切看起来很有前景，并且处于早期阶段，未来可能会非常令人兴奋，但我认为这些工具对产品经理的帮助还没有达到像编码工具对软件工程师的帮助那么大。

So this does treat more the bottleneck on the product management side. It doesn't make sense to me that my partner Mike has this idea that I think is broadly applicable in a couple different ways of like computers can now aggregate humans at scale. And so there's companies like Listen Labs working on this for like consumer research type tasks, right? But you could also use it to, you know, understand tasks for training or for, you know, the data collection piece that you described. When you think about your teams that are in this iteration loop has like the founder profile that makes sense changed over time.
这段话主要讨论了在产品管理方面的瓶颈问题。我的伙伴迈克有一个想法，我认为这个想法在多个方面都很适用。基本上，电脑现在可以在大规模上汇集人力资源。有一些公司，比如Listen Labs，正在致力于这类用于消费者研究任务的工作。但这个技术同样可以被用于理解一些训练任务或数据收集方面的工作。试着想一下，你的团队在不断的迭代过程中，创始人的角色和特质是否随着时间的推移有所变化。

To me, there are so many things that the world used to do in 2022. They just do not work in 2025. So if I often I ask myself, is there anything with doing that today that we're also doing in 2022? And if so, let's take a look and see if it's still going to make sense today because a lot of the workflows in 2003 don't make sense today. So I think today, the technology is moving so fast. Founders, they're on top of genie technology does, you know, tech oriented product leaders. I think are much more likely to succeed than someone that maybe is more business oriented, more business savvy, but it's not a good feel for where AI is going.
对我来说，有很多事情在2022年时世界用来做的方式，在2025年就不再适用了。因此，我常常问自己，我们今天所做的事情中，有没有什么是我们在2022年也在做的？如果有的话，我们需要看看这些做法今天是否仍然合理，因为很多2003年的工作流程在今天已经显得不合时宜。我觉得如今科技发展得非常快，那些掌握前沿科技的创始人和以技术为导向的产品领导者，比起那些可能更懂商业但对人工智能的发展没有好感觉的人来说，更有可能取得成功。

I think unless you have a good feel for what the technology can they cannot do, it's really difficult to think about strategy, whether to lead the company. We believe this too. Yeah, cool. Yeah. Yeah. I think that's like old school Silicon Valley even. Like if you look at gates or Steve Jobs, Slashwasniak, or a lot of the really early pioneers of the semiconductor computer, early internet era, they're all highly technical. And so I must feel like we kind of lost that for a little bit of time. And now it's very clear that you need technical leaders for technology companies.
我认为，如果你对技术的能力和局限性没有很好的理解，要制定公司的战略是很困难的。我们也相信这一点。是啊，很酷。是的，我认为这甚至是老派的硅谷风格。比如，如果你看看比尔·盖茨、史蒂夫·乔布斯、沃兹尼亚克，或者一些半导体计算机和早期互联网时代的先驱，他们都具有很强的技术背景。所以我觉得我们曾经有一段时间失去了这种传统。现在很明显，科技公司需要技术型领导者。

I think we used to think, oh, you know, they've had one exit before. So two exits even. So let's just back that founder again. But I think if that founder has stayed on top of AI, then that's fantastic. But if, you know, and I think part of it is, in moments of technological disruption, AI rapidly changing, that's the rare knowledge. So actually take mobile technology. You know, like everyone kind of knows what a mobile phone can and cannot do. Right. When mobile app is as GPS, all that, everyone kind of knows that.
我认为我们过去可能会想：“哦，他们以前经历过一次成功退出，甚至是两次退出。所以我们应该再支持那个创始人。” 但我觉得如果那个创始人能够紧跟AI的发展，那就太好了。但如果不是，在科技迅速变革的时刻，比如AI的快速变化，这种能力就非常稀有。实际上，想想移动技术，大家基本上知道手机能做什么、不能做什么。比如移动应用的GPS功能，所有人都大概了解这些。

So you don't need to be very technical to have a gutful, can I build a mobile app for that? But AI is changing so rapidly. What could do with voice act with engine? What does it do? How rapidly found? Isch model? Well, it was a reason model. So having that knowledge is a much bigger differentiator, whereas, you know, knowing what a mobile app can do to build a mobile app. Yeah. It's an interesting point because when I look at the biggest mobile apps, they were all started by engineers.
所以你不需要非常专业的技术知识就能有直觉。我可以为此开发一个移动应用吗？但人工智能正迅速变化。配备语音功能的引擎能做什么？它能实现什么？进展有多快？是什么模型？嗯，那是一个有理由的模型。因此，拥有这些知识是一个更大的差异化因素，而你知道，知道一个移动应用能做什么才能去开发它。 是的，这是一个有趣的观点，因为当我观察那些最大的移动应用时，发现它们都是由工程师开创的。

So what's Apple Star by an engineer, Instagram was started by an engineer. I think Travis at Uber was was technicalish. Technically adjacent. Technically adjacent. And Stacarta, poor, if I was an engineer at Amazon. Yeah. And Travis read the insight that GPS enabled a new thing. But so you have to be one of the people that saw GPS on mobile coming early to go and do that. Yeah. You have to be like really aware of the capabilities.
以下是上述内容的中文翻译： 所以苹果是由一位工程师创办的，Instagram也是由一位工程师开启的。我认为Uber的Travis也算是技术出身，至少是与技术密切相关的。Instacart背后的创始人曾经是亚马逊的工程师。Travis之所以能够读懂GPS带来的新机会，是因为他捕捉到了这一新技术的趋势。但要做到这些，你需要是那些能提前预见到移动设备上GPS功能的人之一。是的，你必须对技术的潜力有很深刻的认识。

Yeah. Yeah. I have to know the technology. Yeah. Super interesting. What other characteristics do you think are common? I mean, I know people have been talking about, for example, it almost felt like there was an era where being hard working was kind of poopy or do you think founders have to work hard? Do you think people who succeed? I'm just sort of curious. Like, aggression, hours work, like what else may correlate or not correlate in your mind?
好的。我必须了解这项技术。非常有趣。你认为还有哪些其他特点是常见的呢？比如，我知道人们一直在讨论，曾经有一段时间好像努力工作并不被看重，或者你认为创业者必须努力工作吗？你觉得那些成功的人呢？我只是有点好奇。比如，攻击性、工作时长，还有什么在你看来可能相关或不相关的呢？

You know, I work very hot. The interest in my life where, you know, I encourage others that want to have a drink or rather than like work hot. But even now, I feel like a little bit of nervous is saying that because in some parts of society is considered not politically correct to say, well, working hard pray carl is a personal success. I think it's just a reality. I know that not everyone at every point in their life is in the time when they work hard.
你知道，我工作非常努力。我生活中的兴趣是鼓励那些想要喝酒放松的人，而不仅仅是拼命工作。但是即便如此，我说这些话时还是有点紧张，因为在社会的某些地方，直接说努力工作是个人成功的关键，可能会被认为不够政治正确。我认为这就是现实。我知道，并不是每个人在生命中的每个阶段都能努力工作。

When my kids were first born, that week I did not work very hard. It was fine. Right? So acknowledging that not everyone is in a circumstance in their work hard, just the factual reality is people that work hard accomplish a lot more. But of course, you need to respect people that on the face with it. Yeah, I'd say something maybe a little less correct, which is I less politically correct, which is like, I think there was an era where people thought like there was a there was a statement that startups are for everyone. And like, I do not believe that's true. Right? I think like, you know, you're trying to do a very unreasonable thing, which is like create a lot of value impacting people very quickly. And when you're trying to do an unreasonable thing, you probably have to work pretty hard. Right? And so I think people, I think that got very the sort of work ethic required to like move the needle in the world very quickly disappeared.
当我的孩子刚出生时，那一周我没有很努力工作。这样也没关系，对吧？需要明白的是，并不是每个人都能在工作中非常努力，但事实上，努力工作的人确实会取得更多成就。当然，我们也需要尊重那些面临不同情况的人。我可能会说一些不太政治正确的话，就是我认为曾经有一段时间人们认为创业适合所有人，但我不认为这是对的。我觉得创业就是在做一件很不容易的事情，比如在短时间内创造巨大的价值影响很多人。当你试图做一件不容易的事情时，可能必须非常努力地工作。我认为，快速改变世界所需的那种职业道德已经逐渐消失了。

Yeah. So, those are those are those are hold. I wish I remember who said this, but was it the only people that would change the world are the ones crazy enough to think they can? I think it does take someone with the bonus, the decisiveness that go and say, you know what, does the state of the world? I'm going to take a shot at changing and and and is only people with that conviction of that I think can do this. Thanks me as being true in any endeavor, you know, I used to work as a biologist and I think it's true in biology. I think it's true in technology. I think it's true in almost every field that I've seen is it's the people who work really hard do very well. And then in startups, at least the thing I tended to forget for a while was just how important competitiveness or people who really wanted to compete in win mattered.
是的，我希望我能记得是谁说过这句话，但好像是说，只有那些疯狂到认为自己可以改变世界的人，才能真正做到。我认为确实需要那些敢于面对世界现状并果断行动的人，他们会说：“我要试着改变它。”只有具备这种信念的人，才能实现这种伟大的变化。在任何领域，这都是适用的。我曾是一名生物学家，我认为这一点在生物学中是正确的，在技术领域也是如此，在我见过的几乎每个领域都是如此。那些勤奋工作的人往往表现得非常出色。在初创公司中，我曾一度忘记的一点是，竞争力或真正想竞争取胜的人有多么重要。

And sometimes people come across as really low key, but they still have that drive in that urge and they want to be the ones who are the winners. And so I think that matters. And similarly that was kind of put aside for a little bit, at least from a societal perspective relative to companies. Actually, I've seen I feel I've seen two types. One is they really want their business to win. That's fine. Some do great. Some are they really want their customers to win. And it's so obsessed with serving the customer that that works out. I used to say early physical Sarah. Yeah, yes, I knew about competition blah blah blah. But I was really obsessed with learning this with the customers and that drove a lot of my behaviors that now that that's a really good framework.
有时候，人们表面上看起来很低调，但内心仍然有一股冲劲和渴望，他们想成为赢家。我觉得这很重要。从社会的角度来看，这一点似乎一度被忽视了，尤其是在公司方面。实际上，我看到过两种类型的人。一种是非常渴望自己的业务成功，这没问题，一些人做得很好。另一种是他们非常希望自己的客户能取得成功，他们对服务客户非常专注，因此取得了良好的成果。我以前经常说，早期的时候，我对竞争了解一些，但我真正关心的是学习如何更好地服务客户，这也影响了我很多行为。我认为这是一种很不错的思维框架。

And when I say competition, I don't mean necessarily with other companies, but it's almost like with whatever metric you set for yourself or whatever thing you want to win at or be the best at. Well, when I found this in a startup environment, you just got to make so many decisions every day. You just have to go by gut a lot of time. Right. I feel like, you know, building a startup feels more like playing tennis than solving calculus problems. You just don't have time to think. So make a decision. And I feel like, so this is why people that obsessed day and night with the customer with the company think really deeply and have that construction knowledge that when when someone says, do I ship product feature a of future B?
当我提到竞争时，我并不一定指的是与其他公司竞争，而是指与自己设定的标准或想要赢得和成为最优秀的目标竞争。在创业环境中，我发现每天都需要做出大量决策，很多时候需要依靠直觉。我觉得，创业更像是在打网球，而不是在解微积分题，因为没有时间去思考，只能快速做出决定。这也是为什么那些日以继夜关注客户和公司的人能深思熟虑，并具备建设性知识的人，当有人问你是发布产品功能A还是B时，能快速做出决策的原因。

Yeah, like you just got to know a lot of the time, not always. And it turns out there are so many to use your basis term, like two way doors in startups because frankly, you know, you're very low to lose. So just make a decision and it is wrong. Change of the week later is fine. So I find, but to be really decisive and move really fast, you need to have obsessed usually about the customer, maybe the technology to have that say the knowledge to make really rapid decisions and still be right most of the time. How do you think about that bottleneck in terms of product management that you mentioned or people who have good product instincts because I was talking to one of the best known sort of tech public company CEOs.
是的，就像你知道很多时候，不是总是这样。然而，事实证明在创业公司中，有很多你可以用“两扇门”来形容的情况，因为坦率地说，你几乎没有什么会失去。所以只要做出一个决定，即使错了，也可以在一周后及时更改。这是可以的。但是，要想做决定果断、行动迅速，你通常需要对客户非常着迷，也可能涉及技术，这样你才能有知识去快速做出决策，并且大多数时候是正确的。对于你提到的产品管理瓶颈或者那些有良好产品直觉的人，你是怎么思考的呢？因为我正在和其中一位最知名的科技上市公司首席执行官交谈。

And his view was that in all of Silicon Valley or in all of tech kind of globally, there's probably a few hundred at most great product people. Do you think that's true? Or do you think there's a broader swath of people who are very capable at it? And then how do you find those people? Because I think that's actually a very rare skill set in terms of the people who are, you know, just like there's a 10x engineer, there's 10x product insights it feels. Boy, that's a great question.
他的观点是，在整个硅谷或者全球范围内的科技行业中，可能只有最多几百个出色的产品人才。你认为这是真的吗？还是你认为有更多的人在这方面非常有能力？那么，你如何找到这些人才呢？因为我认为这实际上是一种非常稀有的技能组合，就像有10倍效率的工程师一样，也有人在产品洞察方面达到了10倍的水平。哇，这是个很好的问题。

I feel it's got to be more than a few hundred great product people. Maybe just as I think there are way more than a few hundred great AI people. I think there are. But I think one thing I find is very difficult is that user empathy or that customer empathy because, you know, to form a model of the user or the customer, there's so many sources of data, you run surveys, you talk to handful of people, you remark or reports, you look at people's behavior on other parallel or competing apps, whatever.
我觉得优秀的产品人才远不止几百名。就像我认为优秀的人工智能人才也远不止几百名一样，我相信确实有很多。不过，我发现一个非常困难的事情是用户同理心或者客户同理心。因为，要形成一个关于用户或客户的模型，你需要从很多数据来源获取信息，比如进行调查、与少数人交谈、撰写报告、观察用户在其他类似或竞争应用上的行为等等。

But there's so many sources of data, but to take all these data and get of your own head to form a mental model for what you're maybe I do customer profile or some user you want to serve, think and act so you can very quickly make decisions serve them better. That human empathy, one of my failures, one of the things I did not do well early phase of my career for some dumb reason, I tried to make a bunch of engineers product managers, I gave them product management training and I found that I just foolishly made a bunch of really good engineers feel bad for not being good product managers, right?
有很多数据来源，但要把这些数据整合起来，并跳出固有思维，去形成一个关于你可能要做的客户画像或某个你希望服务的用户的心理模型，以便快速做出决策，从而更好地服务他们。这种人性化的共情能力是我职场早期阶段的一个失误之一。当时我曾愚蠢地试图把一群工程师培养成产品经理，给他们提供产品管理培训，结果发现自己只不过是让这些优秀的工程师因没能成为好的产品经理而感到沮丧，对吧？

But I found that one correlate for whether someone would have good product instincts is that very high human empathy, where you can synthesize loss of signals to really put yourself into the present shoes, to then very rapidly make product decisions and all the sort of. You know, going back to coding assistance, it's really interesting, I think it is like reasonably well known that the cursor team, like they make their decisions actually very instinctively versus spending a lot of time talking to users.
但我发现，一个能够很好地判断产品直觉的人往往具有很高的人类同理心，他们能够整合缺失的信号，真正设身处地地理解他人，从而快速做出产品决策。回到编程助手这个话题上，据我所知，Cursor 团队在做决策时更多依赖直觉，而不是花大量时间与用户沟通交流，我觉得这很有趣。

And I think that makes sense if you are the user and then like your mental model of like yourself and what you want is actually applicable to a lot of people. And similarly, like I think, you know, these things change all the time, but I don't think Cloud Code incorporates despite, you know, scale of usage, feedback, data today from like a trained loop perspective. And I think that surprises people because it is really just like what do we think the product should be at this stage?
我觉得这很合理，如果你是用户，那么你对自己和你想要的东西的心理模型实际上适用于很多人。类似地，我认为这些东西一直在变化，但我不认为 Cloud Code 在今天根据训练循环的视角纳入了使用规模、反馈和数据等方面的信息。我觉得这让很多人感到惊讶，因为这实际上只是我们认为产品在这个阶段应该是什么样的。

So it's also one advantage that starts at half is why you're early, you can serve kind of one user profile. Today, if you're, I don't know, like Google, right? Google serves such a diverse set of user personnel. Now, you really have to think about a lot of different user personnel. Now, since that has complexity, the product changes. But we're starting to get your initial ways in the market.
因此，这也是一个优势，究其原因是如果你处于起步阶段，你可以专注于满足某一种用户类型。今天，如果你像谷歌这样规模的公司，就要面对各种各样不同的用户群体，因此需要考虑众多不同的用户特征。这增加了复杂性，导致产品发生变化。但对于刚进入市场的我们来说，一开始可以专注于特定用户类型。

You know, if you pick even one human that is representative enough, but for broad set of users, and you just build a product for one user that you have one ideal customer profile, one hypothetical person, then you should actually go quite far. And I think that for some of these businesses, be it cluster or cloud code or something, if they have internal via mental picture of a user, that's close enough.
你知道吗，如果你选择一个足够有代表性的人，并为他或她设计产品，那么这个产品实际上可以满足非常广泛的用户群体。只要你为这个理想的客户画像或假想中的用户打造产品，你就能够取得很大的成功。我认为，对于某些业务来说，比如集群或云代码等，如果他们能够在内部形成一个足够接近用户的形象，这就足够了。

So very large your prospective users, you guys, you go really far that way. The other thing that I've observed and curious you guys see this in some of our companies is just like the floor is lava, right? The ground is changing in terms of capability all the time. And the competition is also very fierce in the categories that are already obviously important and have multiple players.
您的潜在用户群体非常庞大，你们有很大的发展空间。我观察到的另一件事，也是我好奇你们是否在一些公司中看到的是，就像“地板是岩浆”这个游戏一样，市场能力正在不断变化。在那些显然很重要并且有多个参与者的领域中，竞争也非常激烈。

So leaders who are really effective in companies a generation ago are not necessarily that effective when recruited to these companies as they're scaling, like because the pace of, it is a velocity of operation or the pace of change. It's interesting to see you say, like I'm looking at what I was doing in like today and in 2022 and saying like, is that still right versus if you're an engineering leader or go to market leader and you've like built your career being really great at how that's done, that may not be applicable anymore.
所以，虽然上一代在公司中非常有效的领导者，如今被招募到这些正在扩张的公司时不一定同样有效，因为运营速度或变化的速度不同。你说这点很有趣，我会看看我在今天和2022年所做的事情，并问自己，这些做法是否仍然合适。而如果你是工程或市场方面的领导者，并且你的职业生涯是建立在擅长这些工作的基础上，那些经验可能再也不适用了。

I think it's a challenge for a lot of people and now many great leaders in lots of different functions still doing things the way they were in 2022 and I think it's just a college change. When new technology comes, I mean, you know, once upon a time there was no such thing as a web search today, would you hire anyone for any road that doesn't know how to search the web?
我认为这对很多人来说是一个挑战，现在许多不同领域的杰出领导人仍然按照他们在2022年的方式做事情。我认为这只是一次思维转变。当新技术出现时，你知道，以前是没有网络搜索这种东西的，但今天你会雇用不会上网搜索的人来做任何工作吗？

I think we're well-possibly that for a lot of job roles, if you can't use OMS in the effective way, you're just much less effective than someone that can. And as a result, everyone in my team, AI find knows how to code. Everyone is a good outcome. And I see for a lot of my team members, you know, when my, I don't know, assistant general counsel or my CFO or my friend that's operator when they learn how to code, they're not software engineers, but they do their job function better because by learning the language of computers, they can now tell a computer more precisely what they want to do for them and computer to do for them and this makes them more effective, their job function.
我认为，对于很多工作岗位来说，如果你不能有效地使用OMS（操作管理系统），你的工作效率就会远低于那些能有效使用的人。因此，我团队中的每个人都知道如何编程，这是一种理想的结果。我注意到，对于我的许多团队成员，比如我的助理总法律顾问、首席财务官或运营伙伴来说，他们学习编程后，虽然不是软件工程师，但他们的工作表现更好了。因为通过学习计算机语言，他们可以更准确地告诉计算机他们想要完成的任务，这使他们在工作中更有效率。

I think the rapid pace of change is disconcerting a lot of people, but I guess, no, no. I feel like when the world is moving at this pace, we just have to change at the world, at the pace in the world. Yeah, to your point, show up in Hires, particularly around product. So, uh, or product and design. So one sort of later stage AI company I'm involved with, they were doing a search for somebody to run product and somebody to run design. And in both cases, they selected for people who really understood how to use some of the vibe coding, such AI, assistant coding tools because they said, they said your point, it's like you can prototype something so rapidly.
我觉得快速变化的节奏让很多人感到不安，但我猜，不，不。我觉得当世界以这样的速度发展时，我们就不得不跟上这样的节奏来改变。是的，就像你说的那样，尤其是在招聘方面，特别是围绕产品的招聘。因此，我参与的一家处于较晚阶段的AI公司，他们正在寻找能够负责产品和设计的人。在两种情况下，他们都选择了真正理解如何使用一些生成代码、助手代码工具的人，因为他们说，正如你说的那样，这能让你非常快速地完成一个原型。

And if you can't even just mock it up really quickly to show what it could look like or feel like or do in a very simple way, you're wasting an enormous amount of time talking and writing of the product requirements document and everything else. And so I do think there's a shift in terms of how do you even think about what processes do you use to develop a product or even pitch it, right? Like what should you show up with to a meeting when you're talking about a product that's going to be fair? Yeah, no, you should have a prototype in some cases. Actually, just give me an example. Resource engineering engineers for a row and hire their interview someone with about 10 years of experience, you know, full-sack, very good resume, also interview the fresh college draft.
如果你无法快速做出一个简单的模型来展示产品的外观、感觉或功能，那么仅仅花时间讨论和撰写产品需求文档以及其他东西就是在浪费大量时间。因此，我认为我们需要改变思维方式，重新考虑如何进行产品开发或推销。比如，当你在会议中讨论一个即将推出的产品时，应该准备什么样的材料？是的，有些情况下你应该准备一个原型。举个例子，公司可能会招聘拥有十年经验的全栈工程师，同时也会面试刚毕业的大学生。

But the difference was the person's 10 years of experience had not used AI tools much at all. Fresh college draft had, and my assessment was the fresh college draft that new AI would be much more productive and I decided to hire them instead to another great decision. Now, the flip side of this is the best engineers I work with today are not fresh college drafts. They're people with, you know, 10, 15 or more years of experience, but they're also really on top of AI tools and that doesn't generally just completely cause their own. So I feel like, I actually think software engineering is a harbinger of what happened in other disciplines because the tools are most advanced in software engineering.
差别在于，那位有10年经验的人并不太使用AI工具，而刚毕业的大学生却已经熟练掌握。这让我认为，这位刚毕业的大学生会更有生产力，因此我决定雇用他们，这也是一个不错的决定。不过，另一方面，我目前合作的最优秀的工程师并不是刚毕业的学生，而是那些有10年、15年或更长经验的人，他们同样非常精通AI工具，这并不会因为他们的经验而被完全替代。所以我觉得，软件工程其实是预示其他领域变化的先兆，因为在软件工程中，这些工具是最先进的。

It's interesting. One company that I guess both of us are involved with this called Harvey, and I led their series B, and when I did that, I called a bunch of their customers and the thing that was most interesting to me about some of those customer calls was because illegal as notorious as being a tough profession for adopting new technology, right? There aren't a dozen great legal software companies. Those customers that I called, which were big law firms or people who were, you know, quite far along in terms of adopting Harvey, they all thought this was a future. They all thought that AI was really going to matter for their vertical.
这很有趣。有一家名叫Harvey的公司，我想我们两个人都与之有联系。我主导了他们的B轮融资。在此过程中，我联系了很多他们的客户。让我感到最有趣的是，法律界历来以难以接受新技术而闻名。毕竟，并没有很多优秀的法律软件公司。但是我联系的那些客户——一些大型律师事务所和已经对Harvey软件采用得很深入的用户——他们都认为这是一种未来趋势。他们都相信人工智能将在他们的行业中发挥重要作用。

And the main thing they would raise is questions like, in a world where this is ubiquitous, suddenly instead of hiring 100 associates, I only hire 10. And how do I think about future partners and who to promote if they don't have a big pool? And so I thought that mindset shift was really interesting. And to your point, I feel like it's percolating into all these markets industries and it's sort of slowly happening, but as industry by industry, people are starting to rethink aspects of their business in really interesting ways. And I'll take a decade, two decades for this transformation to happen. But it's compelling to kind of see how people, like the earliest adopting verticals and something that the people were thinking deepest about it.
他们主要提出的问题是，在一个这种现象普及的世界里，本来可以雇佣100名员工，现在可能只需雇10名。那么，我该如何考虑未来合伙人的选拔和晋升，如果没有一个大的候选人池呢？我觉得这种思维方式的转变非常有趣。而且就如你所说，我感觉这种变化正在慢慢渗透到各个市场和行业，虽然它是逐步发生的，但各行各业的人们已经开始以非常有趣的方式重新思考他们的业务。这种转变可能需要十年或二十年才能实现。但看到那些最早接受变化的行业，以及人们对这些问题的深入思考，确实令人振奋。

It should be really interesting. I think, yeah, I would say about legal startup, Callez's AI, the AI fun help builds, it's doing very well as well. I think the nature of work in the future will be very interesting. So I feel like a lot of teams wound up outsourcing a lot of work, partly because of the costs. But with AI and AI assistants, part of me wonders is a really small, really skilled team with lots of AI tools. Is that going to all perform a much larger and maybe lower cost team that may only not be able to. And they have less coordination cost.
这应该会非常有趣。我想我会说，一个名叫Callez的法律初创公司的AI非常有趣并且表现良好。我认为未来的工作性质会非常有趣。我感觉很多团队最终会外包大量工作，部分原因是成本问题。但随着AI和AI助手的发展，我在想，是否一个小而精干的团队配备许多AI工具，会比一个可能成本更低但规模更大的团队表现更好呢？而且这样的团队协调成本也会更低。

So actually, so some of the most productive teams I'm on, you know, I'm a part of now, is some of the smallest teams, then very small teams of really good engineers with lots of AI enablements and very local things should cost them as well as the other person. So see, we'll see how the world evolves too. We already need to make a call, but you can see where I'm maybe thinking the world may or may not be headed. I work with several teams now. One of which is called Open Evidence and has like a pretty good penetration, like 50% of doctors in the US now, where it's an explicit objective in the company to try to be as small as possible as they grow impact. And, you know, we'll see where these companies land because, you know, there's lots of functions that need to grow in a company over time. But that certainly wasn't an objective for like five years ago.
其实，我现在参与的一些最具生产力的团队，往往是很小的团队。这些团队由一些非常优秀的工程师组成，他们具备丰富的人工智能工具和本地化资源，并且这些资源对他们和其他人都很有帮助。我们将看到世界如何发展。我们需要做出决定，但你可以看出我可能在思考世界可能的发展方向。我目前合作的几个团队中，有一个叫做Open Evidence。在美国，这个团队已经有相当大的影响力，大约50%的医生都在使用他们的产品。公司明确的目标是在增加影响力的同时，尽量保持团队的小规模。我们将拭目以待这些公司会达到什么高度，因为公司中有很多职能需要随时间发展壮大。但这样的目标在五年前是不存在的。

I've heard that objective a lot. I've actually heard that objective a lot in the 2010s. And there's a bunch of companies that I actually think underhired pretty dramatically or stayed profitable and when brag about being profitable for gross, what wasn't as strong as it could be. So I actually feel like that's a trap. How would you calibrate that? Yeah. It's basically really, it's almost are you being laxed asical or too accepting of the progress that your company's making because it's going just fine. It could be going much better, but it's still going great on a relative basis. And so you're like, oh, I'll keep the team small. I'll be super lean. I won't spend any money. Look at me how profitable I am.
我听过很多次这样的目标。特别是在2010年代，我经常听到这样的目标。而且我确实认为，有些公司在人力招聘上非常保守，或者保持盈利但成长不如预期，然后还炫耀自己的盈利能力。所以，我觉得这其实是一个陷阱。你觉得应该如何调整这种心态呢？实际上，这基本上是你是否过于随和或太容易满意于公司现有的进展，因为公司运营得还不错。尽管可能有更大的提升空间，但相对来说已经做得很好了。所以你可能会想，我让团队保持精简，我会非常节省，不会多花钱，然后以盈利能力为荣。

And sometimes it's amazing. Right capital efficiency is great. But sometimes you're actually missing the opportunity or not going as fast as you can. And usually I think what happens is in the early stage of a startup, like you're competing with other startups. And if your way ahead, it feels great. But eventually, if they're incumbents in your market, they come in. And the faster you capture the market and move up market, the less time you give them this sort of realize what's going on and catch on. And so often five, six, seven years in the life of a startup, you're actually competing with incumbents, suddenly, and they just kill you with distribution or other things. And so I think people really missed the mark.
有时候，这真的令人惊叹。使用好资本效率是很重要的，但有时你可能因此错失了机遇，或者没有尽你所能地快速发展。通常，我认为在一家初创企业的早期阶段，你可能是在和其他创业公司竞争。如果你遥遥领先，那感觉很好。但最终，如果你所在市场有行业领先者进入，他们就会出现。你越快地占领市场并向上拓展，他们就越没有时间弄清楚你的策略和行动，并赶上你的步伐。因此，通常在初创企业发展到五、六、七年的阶段，你实际上开始与行业巨头竞争，而他们往往会以分销渠道或其他手段压制你。因此，我认为人们很容易在这一点上失去重点。

And you could argue that was kind of slack versus teams that was, you know, there's a few companies I won't name, but I feel like they're so proud of their profitability and they kind of blew up. I guess on the design side, that was sketch, right? Yeah, but the naming coding, yeah. You know, they were based on the other ones. They were super happy. They were profitable. They were doing great. And then the Figma wave kind of came. Do you think your companies stay this small? Do you think your teams stay this small? Do they my team stay this small?
你可以说，相对于那些公司来说，这有点松散。虽然我不想点名，但我觉得有些公司非常自豪于他们的盈利能力，结果却突然崩溃了。我想在设计方面，这有点冒险，对吧？是的，但在命名编码方面，他们是基于其他公司的。他们当时非常高兴，因为他们盈利并且发展得很好。然后，Figma兴起了。你觉得你的公司会一直保持这样的小规模吗？你觉得你的团队会一直保持这样的小规模吗？我的团队会一直这么小吗？

What do you mean? In terms of just efficiency of like, can you actually get to, you know, affect millions and billions of people with 10, 50, 100 percent teams? I think teams can definitely be smaller now than they used to be, but are we over investing or under this thing? And then also, I think to your point, analysis and market dynamics, right? If there's a, if there's like a winnett take all market, then the incentives just gotta go.
你是什么意思？就效率而言，比如，你能否通过10、50、100人的团队真正影响到数百万甚至数十亿的人？我认为，现在的团队规模确实可以比以前小，但我们是在对这个问题投资过多还是过少呢？此外，我还认为你的观点涉及到分析和市场动态，对吧？如果存在一个"胜者通吃"的市场，那么激励机制就必须要调整。

Yeah, yeah. I'm craft, I think when it sold the Microsoft, was how many people like five people are something? And it sold for a few billion dollars and it was massively used. I think people forget all these examples, right? It's just this, oh, suddenly you can do things really and you could always do something, things lean before. The real question is how much leverage did you have in headcount? How did you distribute? What did you actually need to invest money behind?
是的，是的。我记得 Minecraft，当初卖给微软时，团队好像只有五个人左右吧？然而它却卖了好几十亿美元，并被广泛使用。我想大家常常会忘记这些例子。其实，问题在于，你过去就已经能做到一些事情，只是现在能做得更有效率。关键是你在人力上有多大杠杆作用？你是如何分配资源的？实际上你需要在哪些方面投入资金？

And then I would almost argue that one of the reasons small teams are so efficient with AI is because small teams are efficient in general. Even higher at 30 extra crafty people who get in the way. And I think often people do that. If you look at the big tech companies, for example, right now, many, not all of them, but many of them could probably shrink by 70% and be more effective. Right? And so I do think people also forget the fact that there's AI efficiency, B, there's sort of high value capital being arbitrage into markets that normally wouldn't have them. Legal is a good example. Great engineers didn't want to work in legal. Now they do because of things like Harvey. More healthcare or healthcare, which again, suddenly you have these great people showing up.
然后，我几乎可以说，小团队在使用人工智能方面如此高效的原因之一是因为小团队本身就通常很高效。即使是多增加30个聪明但碍事的人。而我认为，很多时候人们确实这样做。你看看大技术公司，例如，现在，许多（并不是全部）公司可能缩减70%的人手反而会更有效，对吧？此外，我确实认为人们常常忽视了这样的事实：一方面是人工智能的效率，另一方面是高价值的资本被引导到那些通常不会具有这种资本的市场中。法律领域就是个好例子，以前优秀的工程师不愿意从事法律工作，但现在因为像Harvey这样的技术出现，他们愿意了。医疗保健领域也一样，突然之间你会发现有这些优秀的人才加入。

But I think also the other part of it is just small teams tend to be more effective. And AI helps you argue other reasons to keep teams highly small in performance, which I think is kind of underdiscosity. I feel like one of the other reasons why that AI is things so important. I remember one week had two conversations with two different team members. One person came to me to say, hey, I'm going to do this. Can you give me some more headcount to do this? I said no. Later that week, I think independently, someone else, very similar, say, hey, Andrew, can you give me some budget to hire AI to do this? Yes. And so that realization is your high AI, not a lot more humans with this. You just got to have those instincts.
我认为另一方面是小团队往往更高效。AI 可以帮助你找到更多理由维持团队的小规模，从而提高表现，这一点常常被低估。我觉得这是 AI 之所以如此重要的原因之一。我记得有一周，我和两个不同的团队成员进行了两次对话。一个人来找我说，他要做一件事，能不能给他更多的人手。我当时拒绝了。到了那周的晚些时候，又有另一个人独立地来找我，提出类似的需求：他说，"Andrew，我能否获得预算，用 AI 来做这件事？" 对此我回答：可以。这个例子让我意识到，与其增加很多员工，不如利用 AI。这种直觉很重要。

Yeah, that's interesting. If you think of what's happening in software engineering as the harbinger for the next industry transformations, you spend a lot of time investing at the application level or building things there. What do you think is next? What do you want to be next? I feel just a lot of at the tooling level, I feel like I actually prefer a ranked list for all investing in this stuff.
是啊，那很有趣。如果你把软件工程领域正在发生的事情视为下一次行业变革的先兆，那么你会在应用层面上花费大量时间进行投资或构建。那么，你觉得接下来会是什么？你希望接下来发生什么？我觉得在工具层面上的东西很多，我其实更喜欢为所有这些投资列一个优先级列表。

Yeah, does it? One thing I find really interesting, which is a web economist doing all the studies on whether the jobs at high-risk AI disruption. I think you're skeptical. I actually look at them sometimes for inspiration for where we should find ideas to build projects. One of my friends, Eric Brenner, he's here in his company, Work Heelings, which we're involved in. Very insightful in the nature.
好的，这样翻译成中文：是吗？我觉得很有趣的是，有一个网络经济学家在研究哪些工作容易被人工智能颠覆。我觉得你对此持怀疑态度。我有时会查看这些研究，寻找灵感来决定我们应该在哪些方面开展项目。我有个朋友，埃里克·布伦纳，他在他自己的公司Work Heelings工作，我们也参与其中。这项研究非常有深度。

Yeah, I like him. Yeah, good. I find talking to that sometimes useful. Although actually, one of the lessons of learning though is in the top-down market analysis, I think AI was in the talk of vision environment. There's so many ideas that no one's working on yet because the tech of it is so new. So one thing I've learned is AI fun. We have a session with speed. All my life will always end up session of speed, but now we have tools to go even faster than we could.
好的，我喜欢他。嗯，挺好的。有时候和他交流很有用。不过，其实我在学习中得到的一个教训是，在自上而下的市场分析中，AI一直是热门话题。这方面的技术太新了，还有很多想法没人去实现。所以我学到的一件事是，AI很有趣。我们有一个快速学习的环节，我的一生总是以快速学习结束，但是现在我们有工具可以比以前更快。

And so one of the lessons of learning is we really like concrete ideas. So someone says, I did a market analysis. AI would transform healthcare. It's true, but I don't know what to do with that. But if someone has subject matter, X-Fit or an engineer comes and says, have an idea. Look at this part of healthcare operations and drive vision and all this. They go, great, great. That's a concrete idea. I don't know if it's a good idea or a bad idea, but it's concrete. At least we could very efficiently figure out what your customers want to do. It's technically feasible and get going.
学习的一个经验就是我们真的喜欢具体的想法。比如，有人说他们做过市场分析，认为AI会彻底改变医疗行业。这虽然没错，但我不知道该如何着手去实践。但如果某个领域的专家，像X-Fit或一位工程师，提出一个具体想法，例如关注医疗运营中的某个部分并推动愿景发展，大家就会觉得这点子很不错。虽然我不知道这是个好主意还是坏主意，但至少它是具体的。这样一来，我们就能高效地了解客户的需求，看其技术上是否可行，并立即行动起来。

So I find it, hey, I fun. We're trying to decide what to do. We've been a long list of ideas. We try to select your small number that we want to go for on. We don't like looking at ideas that are not concrete. What do you think investing firms or incubation studios like yours will not do two years from now? Like not do manually, sorry. I think there's a lot could be automated.
所以我发现，嘿，我觉得有趣。我们正在努力决定做什么。我们已经列出了一长串想法。我们试图选择其中一小部分我们想要追求的。我们不喜欢看那些不具体的想法。你认为像你这样的投资公司或孵化工作室在两年后会停止做什么事情？不好意思，我指的是停止手动做的事情。我认为有很多事情是可以自动化的。

But the question is whether the task we should be automated. So for example, you know, we don't make follow-on decisions that often, right? Because of portfolio or some dozens of companies. So do we need to fully automate that? Probably not. Because we're very, okay. I'm very hard to automate. I feel like doing deep research on individual companies and competitive research that seems right for automation.
问题在于某些任务是否应该被自动化。例如，我们并不经常做后续决策，对吧？因为我们要管理的投资组合或公司数量有限。所以，我们真的需要完全自动化这个过程吗？可能不需要。因为这个过程还是很依赖人工。我认为，对单个公司的深入研究和竞争研究，更适合被自动化。

I personally use whether I open a SD researcher and other D researcher types of tools a lot to just do at least a cursory market research things. LP reporting, that is a massive amount of paperwork that maybe you could simplify. Yeah. I'm taking the strategy of general avoidance. Besides, you know, basic compliance. You know, one of my partners, Bella, she worked at Bridgewater before, where they had like an internal effort to take a chunk of capital and then try to disrupt what Bridgewater was doing with AI.
我个人经常使用各种SD研究者和其他类型的D研究工具，至少进行一些初步的市场研究。至于LP报告，那真的是一个庞大的文书工作，也许可以简化一下。我现在采取的是一种尽量回避的策略，只是满足基本的合规要求。你知道，我的一个合伙人贝拉以前在桥水工作过，他们曾经尝试通过内部努力，利用一部分资本和AI技术来颠覆桥水的运作模式。

And it's like, you know, macro investing is a very different style. But I think, but I think it probably gives us some indications where the human judgment piece of our business, I think, is not obvious. Like, does an entrepreneur have the qualities that we're looking for when, you know, your resume on paper or your GitHub or, you know, what minor work history you have when you're a new grad? It's not very indicative.
这句话的中文翻译和意思是： 宏观投资是一种非常不同的风格。但我认为，这可能会给我们一些提示，让我们了解在我们的业务中，人类判断力的那部分不是显而易见的。比如，当我们看一个创业者是否具备我们所寻找的特质时，仅凭他们的纸面简历、GitHub或者是刚毕业时有限的工作经历，其实并不具备太多指示性意义。

And so people have other ideas of doing this. Like, I know investors that are like, you know, looking at recordings of meetings with entrepreneurs and seeing if they can get some signal out of like communication style, for example. But I think that part is very hard. I do think you can be like, programmatic about looking at materials, for example. And it's like ranking, you know, quality of teams overall.
所以，人们有其他的想法来做到这一点。比如，我知道有些投资者会观看与创业者的会议录音，看看能否从沟通风格中获取一些信号。不过，我认为这部分非常困难。不过，我确实认为你可以用编程的方法来查看材料，例如，对团队的整体素质进行排名。

Does actually one thing, I feel like our AI models are getting really intelligent. But does it sound like places where humans still have a huge advantage of the AI? It is often if the human has is there has additional context that for whatever reason, the AI model can't get at. And it could be things like meeting the founder and sussing out there, you know, just how they are as a person in the leadership qualities, the communication or whatever. And those things may be reviewing video, maybe eventually we can get that context at AI model. But I find that all these things like as humans, you know, we do a background reference check and someone may as an offhand comment that we catch that affects the decision. Then how does the AI model get disinformation, especially when, you know, a friend will talk to me, but they don't really talk to my AI model. So I find that there are a lot of these tasks where human have a huge information advantage still because they're not figured out the plumbing or whatever it's needed to get information to the AI model.
确实有一点，我觉得我们的AI模型变得非常智能了。但是，在某些领域，人类仍然拥有巨大的优势。这通常是因为人类具备AI模型无法获得的额外背景信息。比如，见到创始人并了解他们作为个人的领导品质、沟通能力等方面的信息。尽管这些信息可能会通过视频评论等方式最终被AI捕捉到，但我发现在很多情况下，人类通过背景调查等方式获取的信息仍然更具优势。比如，有人随口的一句评论可能会影响我们的决策。然而，AI模型如何获取这些信息呢？尤其是一个朋友会跟我交流，但不会跟我的AI模型交流。所以，我发现很多任务中人类仍然拥有巨大的信息优势，因为还没找到获取这些信息的方式或渠道让AI模型学习。

The other thing I think is like very durable is things that rely on like a relationship advantage, right? If I'm convincing somebody to work at one of my companies and they works at a previous company and they trust me because of it or whatever reason, like, you know, all the information in the world about why this is a good opportunity isn't the same thing as me being like Sally, you got to do this, it's going to work. It remains to be seen whether or not company building is actually that correlated with investment returns. But I do think that that side of it feels harder to fully automate. Yeah, yeah, yeah, yeah, yeah, yeah, I think trust because people know people do trust you, right? Trust you, right? Because you can say so many things, it's very easy to lose trust.
我认为另一种非常持久的优势是基于关系的优势。比如，如果我想说服某人在我的公司工作，而他们之前在其他公司时就已经信任我，不管出于什么原因，这种信任感就是一个巨大的优势。即便我提供了所有有关这个机会有多好的信息，也比不过我直接对他们说：“Sally，你应该做这个，一定会成功。” 目前尚不清楚创办公司是否真的与投资回报高度相关，但我确实认为这方面难以完全自动化。人们确实会信任你，而信任是一种很难得却很容易失去的东西。

Yeah, so that makes sense. Yeah, actually one thing I'm curious to take on is, we increasingly see highly technical people try to be first time founders, you know, set up the processes to set up first time founders to learn all the hard lessons and all the craziness needed to be a successful founder. So a lot of time thinking through that, how the set up founders were successful when they have, you know, 80% of the skills needed to be really great, but there's another just a little bit that we can help them with. That's a very manual process. I don't sweat it. You don't sweat it. I just feel it as like a mix of pure groups. Like, can you surround people with other people who are either similar or one or two steps ahead of them on the founding journey? And then the second thing is complimentary hire. I think in general, one of my big learnings is I feel like early in careers, people try to compliment or try to build out the skills that they don't have.
是的，这很有道理。我其实很好奇的一件事是，我们越来越多地看到高技术背景的人尝试创业，成为初次创业的创始人。这需要建立一些流程，让初次创业的创始人能够学到成功所需的所有艰难教训和疯狂的经验。我花了很多时间去思考如何让这些拥有80%必备技能的创始人成功，虽然他们只差一点点，而这是我们可以帮助他们的。这是一个非常手动的过程，我并不担心它，也不认为你需要担心。我觉得这更像是一个纯粹的群体混合体。可以让他们周围有和他们相似或者比他们在创业旅程上领先一两步的人。其次，就是互补的招聘。我认为，总的来说，我的一个重大体会是人们在职业生涯早期通常会尝试去弥补自己所没有的技能。

And later in careers, they lean into what they're really good at and then they hire people to do the rest. And so if the company's working, I think you just hire people, like Bill Gates would notoriously talk about his COO was always the person he'd learn the most off of and then once he does certain level scale, he'd hire his next COO. And so I must via through that lens for founders. Yeah, computerized, we've seen. But I think the best way to learn something is to do it. And so that therefore just go, you know, you'll screw it up. It's fine as long as it's not existential, the business repairs. So I tend to be very elaxidazical. I probably think too many things are existential for companies.
在职业生涯后期，他们会专注于自己真正擅长的领域，并雇佣其他人来处理其余的工作。所以如果公司的运作良好，我认为你就可以雇人，比如比尔·盖茨就曾说过，他的首席运营官是他学习最多的人，然后当公司达到一定规模后，他就会雇佣下一任首席运营官。我认为创始人也可以通过这种方式来运作。我们已经看到了计算机化的趋势。但我认为学习某件事的最佳方式就是亲自去做。因此，只管去尝试，就算搞砸了也没关系，只要不是对业务有生死攸关的影响，是可以修复的。所以我倾向于比较放松，我可能认为有太多事情对公司来说是生死攸关的。

Yeah, it's something. It's like, do you have customers and are you building product? Most of them, yeah. Are you building a product that uses love, right? And then of course, go to market, it's important and all that is important. But you just solve for the product for us. They're usually sometimes you can figure the rest. I grew up most of the time and not always. Yeah, I think there's lots of, there's some counter examples, but yeah, I generally grew up with you. Yeah, sometimes you can build a sucky product. You have a sales channel you can force it through, but I rather know that's not my defile model. I don't know how to make sure I'm just saying that it does work. There's a lot of really bad technology that has big companies right now.
是的，这确实是个问题。就像你是否拥有客户，以及你是否在开发产品？大多数时候是的。你是否在构建一个带着热情去做的产品？当然，进入市场是很重要的，这一切都很重要。但对我们来说，首先要解决的是产品问题。通常情况下，你可以顺利搞定其他事情。我大部分时候是这样长大的，而不是一直如此。是的，我认为有很多反例，但我一般都和你一样。有时候你可以开发一个很糟糕的产品，但如果你有销售渠道，你可以强推它，不过我宁愿知道那不是我定义的模式。我不是很确定怎么确保这一点，只是说这确实有效。现在有很多大型公司使用非常糟糕的技术。

Okay, if you have these, you know, first time, very technical founders with gaps in their knowledge or skill set, being like the core profile of folks, you're backing again. Like, do you augment them somehow? Like, what's what helps them when they begin? I think a lot of things. That's the one they realize that, you know, at venture firms, venture studios, we do so many reps that we just see a lot that even repeat founders have only done twice in their life or even once or twice in their life.
好的，如果你支持的创始人是那些第一次创业、非常技术导向且在知识或技能上有不足的人，那么你会以什么方式帮助他们？我认为有很多方面。在风险投资公司或者创投工作室，我们有很多项目经验，我们见识过的情况远远多于即使是那些二次创业的创始人他们一生中所经历过的。所以我们的经验可以在创始人刚起步时给他们很大的帮助。

So I find that when my firm says, alongside the founders and shares their instincts on, you know, when do we get customer feedback faster, are you really on top of the latest technology trends? How do you just speed things up? How do you fundraise? Most people don't fundraise that much in their lives, right? Most founders just do it handful times. That helps even very good founders with things that because of what we do with more reps and then I think, harming others around the peer group, I know these are things that you guys do.
因此，我发现当我的公司与创始人并肩合作，并分享他们的直觉时，比如如何更快地获取客户反馈，你真的掌握最新的技术趋势吗？你如何加快进度？你如何筹集资金？大多数人在生活中并没有进行很多次筹资，对吧？大多数创始人也只是进行有限的几次筹资。正因为我们的经验丰富，这能帮助即使是非常优秀的创始人。然后，我认为在同行群体中与其他人分享这些经验是很有益的，我知道这些都是你们正在做的事情。

I think there's a lot we could do. It turns out, even the best founders need help. So hopefully, you know, VC's venture studios can provide that to great founders. A lot's wiser about this than I am. I mean, I can't help myself but like want to specifically try to upscale founders on a few things that have to be able to do, like recruiting, right? But I would agree that the higher leverage path is absolutely like you can put people around yourself to do this and to learn it on the job.
我认为我们可以做很多事情。事实证明，即使是最优秀的创业者也需要帮助。所以希望风投和创业工作室能够为优秀的创业者提供支持。很多人对此比我更有经验。我忍不住想特别帮助创业者提升一些他们必须掌握的技能，比如招聘。但我同意，最有效的途径是你可以让周围的人帮助你，并在实践中学习这些技能。

Last question for you. What do you, what do you believe about broad impact of AI over the next five years? Do you think most people don't? I think many people will be much more empowered and much more capable in a few years than they are today. And the capability of individuals is probably, of those in embrace AI, will probably be five greater than most people realize.
最后一个问题。你对未来五年内人工智能的广泛影响有何看法？你认为大多数人对此不认同吗？我认为在未来几年中，很多人将比今天更有能力和更有力量。那些拥抱人工智能的人，他们的能力可能比大多数人预料的要强五倍。

Two years ago, who would have realized that software engineers would be as productive as they are today when they embrace AI? I think in the future, people also do job functions and also for personal tasks. I think people and phrases would just be so much more powerful and so much more capable than they're pretty even imagined.
两年前，谁能想到当软件工程师拥抱人工智能时，他们的工作效率会如此之高呢？我认为在未来，人们不仅会在职业上使用AI，甚至在个人事务中也会如此。我相信人们和他们的表达方式会比现在所能想象的更加强大和更加有能力。

Awesome. Thanks, Andrew. Thanks. Thanks. Thanks. Thanks. Thanks. Find us on Twitter at no priors pod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple podcasts, Spotify or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.
太棒了。谢谢你，Andrew。感谢，感谢，感谢，感谢，感谢。可以在 Twitter 上关注我们，账号是 no priors pod。如果你想见到我们的样子，欢迎订阅我们的 YouTube 频道。你也可以在 Apple 播客、Spotify 或其他收听平台关注节目。这样你每周都会收到新剧集。你还可以在 no-priors.com 注册邮件，或找到每集的文字记录。