为什么近乎免费的智能，也无法改变一切

简要描述Brief Description

这段对话探讨了智能的局限、人工智能的商品化、互补性资产、投资周期、企业采用、投资组合选择以及教育。This conversation examines the limits of intelligence, AI commoditization, complementary assets, investment cycles, business adoption, portfolio choices and education.

目录Table of Contents

• 智能与预测的局限性
  Intelligence and the limits of prediction
• 互补性资产：数据与注意力
  Complementary assets: data and attention
• 赞助商：Ramp、WorkOS 和 Rogo
  Sponsors: Ramp, WorkOS and Rogo
• 前沿实验室、商品化与需求
  Frontier labs, commoditization and demand
• 基础设施投资与资本周期
  Infrastructure investment and capital cycles
• 物理规模、代理与定价风险
  Physical scale, agents and pricing risk
• Sponsors: Vanta and Ridgeline
• 市场、资本配置与制度
  Markets, capital allocation and institutions
• 公司内部的 AI 应用普及
  AI adoption inside companies
• 竞争、利润率与收入分配
  Competition, margins and income distribution
• 投资组合构建与所有权集中
  Portfolio construction and concentrated ownership
• 教育与指导模型
  Education and directing models
• 结束对话与赞助商
  Closing conversation and sponsors

智能与预测的局限性Intelligence and the limits of prediction

我能看到 AI 有这么多令人惊叹的用法，而且我自己就用了海量的 token，也能想象自己还能再多用海量的 token。大家用的 token 都跟我一样多。我们现在的供给实在太紧张了。感觉这会是个规模巨大、永远持续下去的趋势，而且所有参与这轮建设的人都会赚得盆满钵满，对吧？但另一方面，每个 token 的成本又会暴跌，所以也许实际上我们建设得太过头了，对吧？而且我觉得这两种说法我都能讲得头头是道，确信程度也完全一样。I can see all these incredible ways to use AI and I use a good jillion tokens and I can see how I could use a good jillion more. Everyone uses many tokens as me. We are so short. Like it's going to be this huge thing that's going on forever and everyone and everyone who's in this build out is going to make a fortune, right? On the other hand, the cost per token is going to collapse and so maybe actually like we're totally over built, right? And like I can tell I can tell both stories like with equal certainty.

说“这只是智力”大概是我们谈话的主题。请简单描述一下这个想法。Speaker: You called me saying it's just intelligence was sort of the theme of our conversation. Describe that like simple idea.

说话人：有一种看法，认为智力一开始就像变形虫，然后越来越强，接着像猴子，再像人，然后像爱因斯坦，之后就嗖地一下不断提升，最后变成上帝，对吧？我觉得这在某种程度上是个思维错误。人们在这方面错就错在，他们忽略了智力真正能做到什么的限制。也许换种说法：如果你把智力看成一个没有上限的标量，就像树的高度一样，那是不对的。你真正应该把智力看成一种比率，更像球体的圆度。而这个比率所衡量的是：智力是一种工具，我们用它来对宇宙做出预测。Speaker: There's this idea that if intelligence starts at like an amoeba and it gets better and better and then it's like a monkey and then it's it's like a person and then it's like Einstein and then it like goes zoom and like every and it becomes God, right? I think that's something of an intellectual mistake. What people have wrong there is they're missing what are sort of the real limits of what intelligence can do. And maybe another way to put it is like if you think thinking of intelligence as like an unbounded scalar like the height of a tree isn't correct. What it you should really think about intelligence as is more like a ratio like the like the roundness of a ball. And what the ratio is calculating is intelligence is a gadget and we use it to make predictions about the universe.

于是，我们从关于宇宙的一小撮零散事实开始。我们掌握的事实相对有限，而世界，也就是宇宙，是复杂的。我们运用智力在这些事实之间进行推断。随着智力提升，你填补空白的能力也会增强。有些领域确实很适合这样做，对吧？比如数学，你可以——你知道的——解题之类的。在这些领域里，你可以说是接近完美。但对于大多数有趣的事情，也就是世界上的大多数问题，数学术语称之为复杂动态系统。简单来说，它们有点像天气：你不能光靠猜测来解决问题，也无法得出一个封闭形式的答案。你可以尝试对它进行模拟。And so we start with this thin scattering of facts about the universe where we have relatively few of them in the world is in the universe is complex. And we use intelligence to interpolate between them. So as your intelligence goes up, your ability to sort of color in the spaces gets better. And some domains are really amenable to that, right? Mathematics, um you can, you know, solve it like, you know, those sorts of things. And in those areas you kind of get to perfect. Um but for most of the interesting stuff that is most of the questions in in the world. The mathematical term is they're they're complex dynamic systems. So like um what that basically means is it they're kind of like weather in the sense that you can't just guess your way. You can't make a closed form answer. You can try to simulate it.

嗯，但如果你对它进行模拟，模拟的初始条件或后续每一步总会存在一定程度的误差，因为你知道，至少每一步都会被截断。结果就是误差会不断累积，模拟运行一段时间后，结果总会出现一定偏差。所以关键在于，对于把奶油搅进咖啡、预测天气，或者思考一群人——你知道，他们都非常复杂——会如何彼此互动这类事情，你实际上没法做到精确模拟。智能并没有那么强大。因此我的感觉是，智能会不断提升，直到你能够做出某个超级聪明的东西所能做出的所有预测；但事实证明，这只涵盖了你想了解的那些关于世界的有趣事情中相对很小的一部分。Um but if you simulate it, invariably your simulation will have some degree of error in your initial conditions or in each step going forward because you know, at a minimum your steps for example are going to be truncated. And so what happens is your error will build and invariably after the simulation runs for some period of time, you'll be off by some amount. And so the point is that you can't actually like for mixing cream into your coffee or predicting the weather or thinking about how groups of people, you know, who are all very complicated are going to interact with each other, intelligence just isn't that powerful. And so my sense is that what what you get is you get this sort of increasing intelligence to the point where you kind of are making all the predictions that something that super smart could make which it turns out is a relatively small set of all the interesting things you'd want to know about the world.

要突破这一点，你就得走出去，获取更多关于世界的信息，而这需要时间。你得做实验，得亲身接触现实。不过，这也带来一个有意思的推论：随着视角拉远，你可以让智能着力做的一件事，就是降低智能的成本——而我们正在看到这种情况。因此，最终你会发现，基本上每个人都能近乎免费地获得无限量的、非常出色且全面的智能。显然，这只是一个极限情形的说法。它实际上不会免费，但重点是，你知道，我们看到的是，足够出色的智能正迅速变得更便宜，而事实证明，足够出色的智能可以解决绝大多数人们需要运用智能来解决的问题。To get past that you then need to go out and get more information about the world and that just takes time. You have to like do experiments. You have to touch reality. But what one interesting corollary to that is that the zoom actually kind of becomes like one of the things you can turn your intelligence to is reducing the cost of intelligence which we're seeing. So what you actually end up getting is like everyone gets unlimited really great like very round ball so to speak intelligence for essentially free. And obviously that's a sort of a limit statement. It won't actually be free but the point is that you know, that you that what we're seeing is that like really good enough intelligence is rapidly falling in price and it turns out that really good enough intelligence will solve the vast majority of questions where people kind of need to use intelligence.

说话人：是的，你能不能进一步谈谈，为什么“复杂适应系统”或“动态系统”这一概念意味着，即便是完美的智能，或者说我们十年后（或你设定的任何时间范围内）装在瓶子里的那种智能，在应用上仍然会受到限制？Speaker: Yeah, can you say more about why this notion of like a complex adaptive or dynamic system means that actually even perfect intelligence or whatever intelligence we have in a in a bottle 10 years from now or pick your horizon will still be limited in its applications?

说话者：对，所以这就像是非线性动态系统。关于这些系统是如何被发现的，以及它们如何成为数学的一个分支——混沌理论，有个著名的故事。很久以前，有个人在开发一个天气预报模型。那时候用的是占满整间屋子的大型计算机，诸如此类。他运行模型，模型会预测未来一段时间内的气压和温度变化。他觉得：“不错。”于是，为了确认模型还能正常运行，他把它重置后又运行了一遍。起初，结果基本相同，但随后结果开始出现偏差。到了预报的第三、第四、第五天左右，结果已经完全不同了。那个人心想，大概是：“糟了，我的系统肯定有个 bug。”Speaker: Yeah, so like non-linear dynamic system. So the famous story here and how these were discovered and this this came into the branch of mathematics became chaos theory is um a back in the day there was a um there was a guy who who was building a weather forecasting model, you know, back on big computers that took up rooms and that sort of thing. And he runs the model and it makes predictions of pressure and temperature over some span of some forecasting period. And he's like, "Cool." And so then, just to make sure it's still working, they reset it and they run it again. And it starts out basically the same results, but then it's the results start to diverge and by sort of the third, fourth, fifth day of the forecast, um the results were entirely different. And the guy thought, you know, some version of like, "Darn, I must have a bug in my system."

嗯，然后就开始试图弄清楚，为什么计算机在反复运行时得出的结果不一样。后来发现，在第二次运行时，负责将数据载入计算机的技术员把压力、温度等所有初始条件都四舍五入了，精度少了一位小数。你可能会觉得，好吧，这或许会在整个预测期内造成一个小而稳定的误差。但实际情况是，这个小误差在每一步反复计算中——也就是根据当前条件计算下一组条件——不断累积，结果在预测期开始后不久，预测就完全不同了，天气状况也变得面目全非。Um and begins, you know, the process of trying to figure out why the computer isn't producing the same results in repeated runs. And it would turned out that the technician who had loaded the data into the computer in the um second run had rounded off some at some decimal, like one decimal less, all of the initial conditions for pressure and temperature and such. And you would have thought that okay, maybe that would have produced a small but consistent error over the whole forecast period. But what had actually happened was that small error through all of the, you know, uh repeated steps, you take your current conditions, you calculate your next set of conditions, they compound and you had an entirely different forecast by some small distance into the into the forecasting period and the unrecognizable weather.

一个模型说天气是70度、阳光明媚，另一个模型说天气是，你知道，20度、倾盆大雨。人们把这称作蝴蝶效应，因为至少原则上，你知道，一只蝴蝶在非洲扇动翅膀，可能就会导致北美发生飓风，诸如此类。结果发现，我们感兴趣的许多系统都有这种特性。你可以知道关于气压和温度的一切，但你无法建模——你不一定能写出一个可以轻易求解的方程，告诉你会不会发生飓风。你得对它进行建模。而模型本身的粒度也会有一定限制，除非你真的进一步把整个宇宙都纳入其中。所以归根结底，如果你想模拟整个宇宙，你实际需要的就是整个宇宙，对吧？One one one model said it was 70° and sunny and the other model said it was, you know, 20° and pouring. People called it like the butterfly effect cuz at least in principle, you know, a butterfly could flap its wings in Africa and like that could lead to a hurricane in in North in in North America or whatnot. And it just turns out that like many systems that we're interested in like have this characteristic. You can know everything about pressure and temperature, but you can't model you can't necessarily like write down an equation that you can solve easily that would tell you that there's going to be a hurricane. You have to like model it. And the model in turn is going to be grained at some level unless you actually go to the step of actually having like the whole universe. And so ultimately like in the limit, if you want to simulate the whole universe, what you actually need is the whole universe, right?

现在，你实际上不能——你其实不会那样做。你真正会做的是，嗯，有很多很多情况下，模拟对于进行近似计算非常有用。呃，在这些情况下，你知道，可以运用智能来解决问题，但还有更多情况行不通。嗯，所以，智能是有限的；这种限制并非源于宇宙本质上不可知——我想可以这么说。回到“这只是智能”这个说法。从更务实的角度来看，你觉得这意味着我们会走向何方？Now, you can't actually you don't actually do that. What you actually do is, you know, there are many, many circumstances where simulation is super useful for approximations. Uh and in those circumstances, you know, you can use intelligence to solve them, but there are many more um where it doesn't work. Um and so, intelligence is limited in that instead of the sort of fundamental unknowableness, I guess, about the universe. So, back to this notion of it's just intelligence. What does that mean about where you think we're going from a more practical standpoint?

互补资产：数据与注意力Complementary assets: data and attention

说话人：我们都会在口袋里拥有一支全职工作的团队，团队成员的智商都高达 200。然后这支团队本身也会有一支可以调遣的团队，成员人数还要多得多。他们会试着找出所有那些我们懒得去做的小优化。而且每家公司都会为员工提供类似的团队，帮助他们完成工作。有很多企业并不适合这样做。我觉得，嗯，方式有很多种。所以我认为，在很多公司里，情况是这样的：消费者得到满足，对吧？他们——消费者目前的理性是有限的，对吧？他们的认知空间就那么大。所以，你会去找消费者说：“听着，我们来帮你花这笔钱。”Speaker: We're all going to have, you know, a team of, you know, 200 IQ people working full-time in our pocket, and that team will have, you know, it itself will have a team of that it can deploy of many more. And they're going to be trying to figure out all the little optimizations that we're too lazy to do. And every company, you know, will will have will make similar teams available to their employees in order to, you know, to do their jobs. There's just a lot of businesses that where that's not good for them. I think there's um you know, variety of ways. So, in a lot of companies, I think that what happens is like consumer satisfies, right? They Consumers are boundedly rational at present, right? They only have so much cognitive space. And so, you sort of go to a consumer and you say, "Look, we'll take your money.

“我们不会把钱偷走。我们会给你一个不错的利率。就是说，你不用操心。你照常过日子就行。”消费者不会说：“听着，如果我每天都琢磨这件事，我一年就能省下大概 400 美元。所以，我每天都得琢磨这件事。”消费者会说：“嗯，听起来还不错。”对吧？我继续忙自己的事了。但有了智能体，你会觉得它会、会、会持续为他们优化。而且有些公司从根本上说，生产出来的东西就是某种产物，像是某种凝结起来的智能。所以，我绝不是第一个意识到 AI 可能会对某些软件公司造成不利影响的人，但你知道，我只是想到这么一点：AI 模型本质上有点像一种通用智能工具。We won't steal it. We'll pay you a decent interest rate. Like, you don't have to think about it. Like, and you can go about your life." And the consumer doesn't say, "Look, if I think about this every day, I could save, you know, like, $400 a year. So, therefore, I'm going to have to think about this every day." The consumer says something like, "Eh, sounds good enough." Right? I'm going to move on. But with an agent, like, you would think the agent will will will continuously optimize for them. And there's also companies where what they produce fundamentally is like some sort of artifact that is just like some sort of crystallized intelligence. So, I'm by far not the first person to realize that AI could be bad for certain software companies, but like, you know, just this idea that like what what an AI model is it's kind of like this general-purpose intelligence tool.

很多软件工具其实就像是已经结晶、固化下来的片段，是智能的投射。所以，你知道，我一直在思考公司和投资时，发现一种很有帮助的做法：假设每个人都拥有极强的智能，然后问，还剩下什么？因此，人们谈到某种反驳时，我常听到这样的说法：你不明白这有多难，不明白这有多复杂，也不明白有多少聪明人不得不为此思考了这么久。而我那种有点笼统、但大致没错的回应是：好吧，当然，但归根结底那都只是智能，对吧？嗯，如果把这套逻辑贯彻到底，那么一旦智能变得廉价且无处不在，你真正想拥有的就是智能的互补品。And what many software sort of tools are are kind of like crystallized and hardened like pieces, like, projections of intelligence. And so, you know, as I've been thinking about companies, you know, and investments, I found it helpful to sort of you know, assume everyone has really great intelligence and ask what's left. And so, the line I've been using when people talk about some version of the pushback is often some version of but you don't understand how hard this is, you don't understand how complicated this is, and you don't understand, you know, how much how many smart people have had to think for so long about this. And I sort of my my my sort of hand-wavy and sort of approximately right response is, you know, okay, sure, but that's all just intelligence, right? Um and to follow this logic all the way through to the end is if intelligence becomes cheap and ubiquitous, what you want to own are the complements to intelligence.

所以，智能的互补因素是，嗯，它们是数据、注意力，比如爱、感情，你知道，任何你想要的情感依恋之类的东西。嗯，还有物理上的接近性、规模，你知道，这说起来有点愤世嫉俗，但还有监管俘获之类的事情。你知道，对于某些企业来说，情况是这样的：它们部署智能的能力有限，可能是经济因素，比如成本；它们的公司规模就那么大，也没法在十年里用上谷歌最优秀的 1 万名工程师；也可能只是组织发展滞后，诸如此类。比如说，让你的企业能够部署多得多的智能，让竞争对手也能在他们的企业里部署多得多的智能，让客户也能运用大量智能，最终可能对你更有利。或者，如果智能是你服务客户的一道门槛——也就是说，你缺少智能——那可能会对你不利；又或者，如果客户的惰性恰恰是构成你经济价值的一个根本要素，那么智能也可能对你不利。And so, the complements to intelligence are um the they're they're data, attention, like love, affection, you know, whatever sort of emotional attachment you want. Um it's it's physical proximity, it's scale, you know, it's a little cynical, but it's it's it's regulatory capture, it you know, it's these sorts of things where for some businesses, what's what happens is their finite ability to deploy intelligence, be it economics, you know, just the cost of it, they're only a certain size company and they don't have access to 10,000 of Google's best engineers for 10 years, or be it just the organization is behind or whatever. Like the ability to deploy way more intelligence to your business, and your competitors to deploy way more intelligence to their business, and your customers to use lots of intelligence, it can work out to be, you know, better for you, or you know, if intelligence is a barrier to you serving your customers, like the lack of it, or it can be um bad for you if intelligence if your customer's lethargy is kind of a fundamental element of your of your economic um value.

说话人：也许可以选 Meta 之类的公司，作为一个很好的例子。我觉得你认为这家公司拥有与……完美互补的东西。Speaker: Maybe pick like Meta or something as like the perfect example of a company that I think you believe owns perfect complements to

说话者：好的。Speaker: Sure.

发言者：无处不在、成本低廉的智能，只是想让这个例子更鲜活一些。Speaker: ubiquitous cheap intelligence, just to like bring the example to life.

说话人：我先暂时把 Muse、智能体之类的东西放一边，专注谈谈业务。嗯，Meta 的核心业务，对吧，就是个性化电视。Speaker: I'm actually going to set aside for a moment like muse and agents and that whole thing and focus on business. Uh and the core business at Meta, right, is personalized TV.

发言人：个性化电视。Speaker: Personalized TV.

发言人：没错，它过去是社交媒体，但现在更像是个性化电视。结果发现，你知道，人们真的很喜欢看 Instagram 之类的内容。它刚发现我特别喜欢英式黑色幽默，这可太棒了。要是你把视角拉得特别高——比如大家常说的万英尺高空视角，或者别的什么——我们就来聊聊从太空看 Meta，对吧？也就是最高层面的视角。他们的营收有 2500 亿美元，每年也要花掉 2500 亿美元。其中一部分会在税务上计入成本，另一部分会计入资本支出。这其实不重要，总之他们把钱全花出去了。你可能会问，他们把钱都花在哪儿了？一种极简的概括是：他们把钱全花在智能上。我这是什么意思呢？他们在买数据中心。Speaker: Yeah, it used to be social media, but now it's like it's personalized TV. And it turns out that, you know, people really like uh watching Instagram or whatnot. It just discovered that I love like dark British humor and it's great. If you take the a really high like you people talk about like the 10,000-ft or whatever. Let's talk about like Meta from space, right? Like the highest view. They have 250 billion in revenue. And they spend 250 billion dollars a year. Some of it gets tax accounted as for its costs, some of it is accounted for its capex. It doesn't really matter. Just they spend it all. The question you might ask is like what do they spend it on? And a really reductionist view is that they spend it all on intelligence. And what do I mean by that? Well, they're buying data centers.

什么是数据中心？嗯，它有点像一种智能生产工具。它们还在做什么？嗯，它们实际上就是在购买推理能力，对吧？那就是智能。它们还在做什么？嗯，它们花钱雇非常聪明的人来编写算法。那就是智能。它们还在写代码，这也是智能，对吧？比如它们在审查视频。这肯定算是与智能相关的东西。它就是智能。所以，从某种高度简化的角度来看——当然，你不应该把这些说法当真——我只是想强调这里几个非常大的趋势。它们买的全都是智能。如果你往前看，考虑到未来五年左右，智能的价格会下降大约一百倍。What is a data center? Well, it's kind of an intelligent producing tool. What else are they doing? Well, they're they're actually literally buying inference, right? That's just intelligence. What else are they doing? Well, they're paying really smart people to write algorithms. That's intelligence. They're writing code, also intelligence, right? Like they're surveying videos. That's intelligence-adjacent, sure. It's intelligence. And so in some highly reductionist sense and you shouldn't you shouldn't take these things literally, but I'm just kind of trying to highlight the really big directions here. All they buy is intelligence. And if you think forward with the fact that intelligence the price of intelligence is falling by, you know, like a factor of a hundred or something over the next five years.

从第一性原理出发，成本在某种程度上就会消失。你知道，这只是从成本方面来考虑。嗯，从收入方面来看，所有创作者都能从中受益。你就是一位创作者。每个为 Meta 制作内容的创作者，如果能用上成本只有原来百分之一、效率却高出一万倍的智能技术，或者类似的东西，能创作出多少更优质的内容？对吧？重点大概是，效果会更好。接下来，Meta 也许不会把成本降到零，也许他们会投入更多，或者至少会投入一些成本。他们可以更好地挑选接下来展示给用户的视频，这应该能让内容更精准地触达目标受众，也更有趣。嗯，当然，广告方面也是如此：广告主可以设计出效果好得多、定位精准得多、个性化程度高得多的广告，而 Meta 也就能更好地投放这些广告。First principles, their costs kind of go away. And you know, that's just thinking about the sort of the cost side of things. Um, on the revenue side of things, there are all of the creators. You are a creator. How much more quality um, creative can every creator that makes content for Meta make if they have access to intelligence that's 1/100 the cost and 10,000 times more effective or whatever, right? The point is probably better. And then Meta in turn, maybe they don't take their costs to zero. Maybe they spend more or more than zero. And they do a far better job of picking the videos to show people next, which should make the content more, um, you know, better targeted and more enjoyable. Um, and then of course on the ad side, um, you know, advertisers could make far better designed, far better targeted, personalized ads that then Meta in turn can target much better.

广告商也可以构建更迂回、更难以捉摸的转化漏斗之类的东西。所有这些的核心在于，也许他们不必把成本降到零；如果凭借更强大的智能和低得多的价格，他们或许能多卖出一些广告。这一切都取决于他们所拥有的东西，而这些东西不会被无处不在的智能摧毁，那就是数据和注意力。他们拥有海量数据，了解这些人，以及他们如何观看视频之类的内容。他们还拥有大量注意力，而这两者会相互叠加。因此，我不认为廉价且无处不在的智能会让人们不再喜欢看那些有趣的20秒短视频。嗯，所以说不定他们还能从人们用来做其他事情的时间里，边边角角地争取到一些时间。And advertisers can also build kind of much more slippery funnels or whatever to convert. The whole point of all this is that maybe instead of taking their cost to zero, if with much better intelligence and much lower prices, maybe they can like sell a few more ads. All of this is downstream of what they possess, which is not, um, destroyed by ubiquitous intelligence, which is data and attention. They just have this enormous amounts of data about, you know, all all these people and how they view they view videos and whatnot. And they have an enormous amount of attention and these things compound each other. And so I don't think that the availability of cheap and ubiquitous intelligence is going to cause people not to be interested in watching funny their 20-second videos. Um, and so if anything, you know, they can win some time on the margin, you know, um, from other things people do with their time.

我其实并不持有它的股票，不过你知道，理论上来说，如果拥有更多智能、能从中受益的公司之一就是 Grindr，对吧？嗯，基本上，Grindr 是一款同志交友应用，但在它的发展历程中，它一直受限于一个事实：它终究不是一家特别大的公司。结果就是，他们获得了大量关注，也拥有海量数据，嗯，但他们能做的事情也受限于团队规模、预算，以及创造新事物的能力——这些归根结底都是有限的。嗯，你知道，这在某种程度上意味着，Grindr 没法成为你想象中它可能成为的一切。再说一次，我其实并没有持有 Grindr 的股票什么的，不过我们会研究很多公司，我自己也会研究很多公司。就这么说吧，我突然想到，这又是一个例子：嗯，你知道，这里的思维模型大概是，如果你有谷歌最优秀的 1 万名工程师，让他们花 5 年时间来解决你的问题，你能做到什么？I don't actually own it, but like, you know, a company that benefits in theory from having a lot more intelligence is Grindr, right? Um, and you know, basically right, Grindr is the gay dating app, but it has been constrained over its life by the fact that it's just not that big a company. And it turns out they've got lots of attention and they've got lots of data, um, but their ability to build all the things that you could imagine building on what ultimately amounts to sort of a finite size, you know, team and budget and ability to like create things. Um, you know, it has has meant in some ways that Grindr can't be all the things you could imagine it could be. And again, I don't I don't actually own any Grindr or whatever, but we study I study lots of companies and let's just say it occurred to me that like this is another example of something where, um, you know, here the here the sort of mental model is like what what if you had 10,000 of Google's best engineers to go work on your problem for 5 years, like what could you do?

至于 Grindr，你知道，也许他们还可以做得更多。And Grindr is one where you, you know, maybe they could do more.

赞助商：Ramp、WorkOS 和 RogoSponsors: Ramp, WorkOS and Rogo

发言人：Ramp 是唯一一个旨在让财务团队更精简、更高效、表现更出色的平台，平均每年为企业节省 5%，让 So 能够专注于增长。Ramp 刚刚在英国正式开展业务。如果你在英国经营企业，现在就可以使用 Ramp 的 AI 驱动财务平台，在一个地方管理公司卡、费用、账单、审批和会计。我用 Ramp 经营我的企业，你也应该这样做。了解更多信息，请访问 ramp.com/invest。 OpenAI、Cursor、Anthropic、Perplexity 和 Vercel 有一个共同点：它们都在使用 WorkOS。原因如下：要大规模赢得企业客户，你必须提供 SSO、SCIM、RBAC 和审计日志等核心功能。这正是 WorkOS 的用武之地。你无需花几个月自行构建这些关键功能，只需使用 WorkOS API，从第一天起就能获得所有这些功能。Speaker: Ramp is the only platform built to make your finance team leaner, faster, and better, saving businesses 5% annually on average so So can stay focused on growth. Ramp just opened for business in the United Kingdom. So if you're running a business in the UK, you can now use Ramp's AI-powered finance platform to manage cards, expenses, bills, approvals, and accounting all in one place. I run my business on Ramp and so should you. Learn more at ramp.com/invest. OpenAI, Cursor, Anthropic, Perplexity, and Vercel all have something in common. They all use WorkOS. And here's why. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, SCIM, RBAC, and audit logs. That's where WorkOS comes in. Instead of spending months building these mission-critical capabilities yourself, you can just use WorkOS APIs to gain all of them on day zero.

这就是为什么你听说过的许多顶尖 AI 团队早已在使用 WorkOS。WorkOS 是让产品快速达到企业级要求的最佳途径，让你专注于最重要的事：打造产品。访问 workos.com，即可开始使用。 Rogo 是专为金融机构打造的 AI 平台，为全球数百家领先企业的数万名银行家和投资者提供服务。Rogo 的 AI 智能体能够自主执行企业大量工作流程，包括筛选交易、起草 SIM、开展买方联络，以及对数据室进行尽职调查。整个过程都具备全面的安全与合规保障。每项操作都会为 Rogo Intelligence（企业的上下文层）提供信息，将多年的交易经验转化为安全、受治理的机构记忆，让每一笔新交易都比上一笔更出色。That's why so many of the top AI teams you hear about already run on WorkOS. WorkOS is the fastest way to become enterprise-ready and stay focused on what matters most, your product. Visit workos.com to get started. Rogo is the AI platform purpose-built for financial institutions serving tens of thousands of bankers and investors at hundreds of leading firms worldwide. Rogo's AI agents autonomously execute large chunk of your firm's workflows. They can screen deals, draft SIMs, run buyer outreach, and due diligence on data rooms. All with full security and compliance handled. Every action feeds Rogo intelligence, the firm's context layer, turning years of deal experience into a secure, governed institutional memory that makes each new transaction stronger than the last.

如需了解更多信息，请访问 rogo.com/invest。To learn more, visit rogo.com/invest.

前沿实验室、商品化与需求Frontier labs, commoditization and demand

说话人：我想聊聊这些实验室。回到最初的话题，我觉得很多优秀的投资者会说，嗯，智能，尤其是前沿智能，并不是一种大宗商品。从历史上看，大家讨论的一直是闭源和开源之间的智能差距。如今，讨论越来越多地转向：实验室公开提供的东西，与它们在自家业务内部私下使用的东西之间，存在怎样的差距。它们似乎已经摸索出如何阻止或减缓蒸馏。很多人会说，眼下正在发生一种所谓的逃逸速度现象，前沿 token 将会更有价值、更有用，而实验室自己也能蒸馏自己的模型，同时提供最好的廉价智能版本。我很好奇，能不能请你从投资者的视角，畅谈一下如何看待这些实验室作为公司的情况？Speaker: I want to talk about the labs. So back to the original point, I think lots of great investors would say um intelligence, specifically frontier intelligence, is not a commodity. Historically, the conversation has been about the gap in intelligence between closed-source and open-source. Increasingly, the conversation is about the gap between what's publicly available from the labs and what's privately used by them in inside their own businesses. They seem to have figured out how to stop or slow distillation. Lots of people would say that there's this escape velocity thing happening and that the frontier token is going to be way more valuable and useful and that the labs themselves can then distill their own models and provide the best version of the cheap intelligence, too. I'm I'm curious for you to riff on the labs as companies through this sort of mind of an investor.

说话者：如果他们拥有更好的模型，而且能以远低于其他人的成本，生成某种足够好的智能，同时又没人能复制，那么这会是一家非常棒的——这会是一门非常棒的生意。Speaker: If they have models that are better and the ability to produce sort of adequate intelligence at way lower cost than everybody else and nobody can copy it, then it's going to be a great It's going to be a great business.

发言者：我想我有几点想法。你知道，其中一点是，我看着模型被部署的过程，感觉可以这样理解：一开始，这些模型能力很不足，基本上只能完成很少一类任务。然后模型会不断变好，逐步向技术栈上层发展。每上一个台阶，前沿模型就开拓了更大的、你可以称之为可触达市场的市场。但与此同时，背后发生的是，一旦模型达到饱和，一旦某种东西——不管是什么——饱和之后，你就会发现，你绝不会把前沿模型用在客服应用里的聊天机器人上。那样做简直太蠢了。所以，正在发生的事情是，那些领域显然会逐渐商品化。于是就形成了一波需要前沿模型才能应对的难题，而这波难题后面的任务就不需要了。Speaker: I guess I have a couple of thoughts. You know, one is as I've watched models get deployed, it feels like one way to think about it is like you start out with these very inadequate models that are basically able to do a very small set of jobs. Then what happens is the models get better and they work their way kind of up the stack. And it with each successive step up, you know, the frontier model has unlocked a larger, you know, call it like addressable market. But behind it, what's happening is that once the models get saturate once the sort of whatever get saturated, you get this like you know, you would never use a frontier model for like a chatbot in a in a customer service application. You'd be an idiot. And so what's happening is um those like areas, they clearly commoditize. And so there's like this wave of hard tasks where you really need a frontier model and then behind that you don't.

所以，你知道，我那些看好前沿实验室公司的朋友——他们都是很看好这类公司的多头，或者不管怎么说——显然，这些公司的财务数据并未公开之类的，他们可能比我掌握更多信息。但他们会说类似这样的话：“别担心，我们也能沿着整个效率前沿提供更便宜的 token。”也许他们现在就能做到，也许现在还做不到；也许几年后能做到，也许永远做不到。我觉得这件事很难想明白。当然，趋势一直是这些东西在迅速商品化。如果你看看中国模型的崛起，以及每一步、每一步的进展，就会发现这一点。呃，至少成本一直在暴跌。所以，嗯，即便他们在某个领域占据优势，能用 token 来完成那些远远落后于前沿的任务，这个市场可能也不会很大，嗯，因为从整体来看，提供这类服务的成本实在不高；而且竞争很激烈，即便他们在一个不断萎缩的商品市场中拥有成本优势。And so, you know, the friends I have who who are big, you know, frontier labs companies bulls or whatever and obviously these companies' financials aren't public or whatever and they might have an information advantage over me, but would say something like, "Don't worry, we can also along the whole efficient frontier produce cheaper tokens." Maybe they can right now, maybe they can't right now, maybe they will in a few years, maybe they won't. It's very I find this this to be very hard to think about. Certainly the trend has been rapid commoditization of these things. If you would look at the rise of Chinese models and the sort of the step every step. Uh and at a minimum, the cost has been collapsing. So, um even if they do sort of have a position where the tokens necessary to do tasks that are sort of well behind the frontier, it might not be a very big market um because it just doesn't cost that much in the scheme of things to deliver and it is competitive even if it's um even if they are a cost leader in a commodity in a shrinking commodity market.

还有一件事，我觉得编程是模型的一项巨大用途。随着你不断推进，问题就变成了：在接下来、再接下来、再下一层智能上，你还能继续拓展出越来越大的应用领域吗？乐观派的说法可能是：当然，人们愿意为一个能想出如何治愈癌症的模型付出任何代价。也许确实如此，但回到我们之前的谈话，我觉得要想出如何治愈癌症，就得接触现实，而我不确定更高的智商能不能帮你做到这一点。如果你不相信还存在某些极其惊人的应用场景，让你能为下一代、更高智能的模型收取天价，那么你就会逐渐走到这样一个阶段：比如说，你已经有了编程，接下来或许是……我也不知道，别的什么；但之后就会进入更小众的领域。And then the other thing is that I think that you know programming was this huge use of models. As you push forward the question becomes like for the next next next like layers of intelligence do you still have bigger and bigger places to go after and the bull story would be some version of like yes like people will pay anything for a model that can like figure out how to cure cancer. Maybe that's true but I think figuring out to go to our prior conversation I think figuring out how to cure cancer requires touching reality and I'm not sure that more IQ gets you that. If you don't believe that there's some super incredible use cases where you can just charge an enormous amount for the next for like more intelligence then you start getting to this point where like the next step up in terms of like you got coding and then maybe it's like I don't know something else and then but it gets into smaller niches.

它就像一个菱形的需求前沿：一开始，用途越来越广；但实际上，后来就变得越来越小众。你可能会开始问，自己到底有多常需要一个 IQ 达到 300 的人？你知道，这些事情我们都会慢慢弄清楚，我不想太武断，但我觉得最后很可能会是这样：超级精英级智能的用途寥寥无几，而非常出色的智能则有很多用途。这种非常出色的智能竞争十分激烈，也会变成一种标准化商品。如果这些前沿实验室能设法成为提供这类智能的成本领先者，那确实有利润空间；但与此同时，也会发生大规模的通缩。接下来问题就变成了：你知道，如果足够好的智能变得非常非常便宜，人们到底会买多少这种足够好的智能？It's like this diamond shaped demand frontier where like initially you've got wider and wider uses and then actually it starts to get more and more rarified where you actually like how often do you really need a 300 IQ one might start to ask and you know look these things are all going to like we're going to find out and I don't want to be too dogmatic but it strikes me as like totally reasonable that what we end up with is some version of like there are very few use cases for like super elite intelligence. There are lots of use cases for like very good intelligence. That very good intelligence is highly competitive and commoditized and if these frontier labs can somehow become like cost leaders in doing that there's a margin to make in being like a cost leader but then there's also just rampant deflation and then this question becomes of like you know how big is the market if you know good enough intelligence is very very cheap then how much good enough intelligence are people going to buy?

发言人：我就继续唱反调吧。Speaker: I'll just keep playing the devil's advocate.

说话者：是的。Speaker: Yeah.

发言者：所以在这种情况下，人们可能会说的两件事，一是杰文斯悖论，对吧？也就是说，随着它变得更便宜，我们只会想用得更多。所以我很好奇你怎么看。第二件事是，你知道，历史上，智能的新用途通常都是由非常聪明的人发现的。我的一位好朋友说，智能会创造自身的需求。你怎么看？比如说，如果是人类在提出新的问题，而智能随后被用来解答这些问题，那么对人类来说，它或许会呈菱形分布。但如果是智能本身在提出新的问题，而我们把它训练得非常擅长这一点，让它以一种开放式的方式去探索，就像全人类都会做的那样，只不过历史上是由那些智商达到 150 以上的人带头。Speaker: So the two things people might say in this case are Jevons paradox right that like as it gets cheaper we're just going to want to use more of it so I'm curious what you think about that. The second would be you know historically the new uses of intelligence have typically been discovered by highly intelligent people. My my good friend says intelligence creates its own demand. What about that? Like what about the fact that maybe it's diamond-shaped for humans if humans are driving the new questions that the intelligence is set after to answer. But what if the thing itself is creating the new questions and we train it to do that really effectively and just sort of like explore in an open-ended way in the way that like all of humanity would be doing led by the people with, you know, 150 plus IQ or something historically.

发言者：杰文斯悖论，对吧？这只是需求价格弹性的一个比较狭义的例子。一般来说，所有市场都呈 U 形，对吧？比如，产品一开始很糟糕的时候，嗯，没有需求。后来产品变好了，人们愿意花很多钱买，但嗯，他们买的量不会很大。再后来，随着价格下降，市场规模就会扩大，因为，你知道，你知道，这基本上就成了杰文斯悖论。但接着你会到达一个临界点：进一步降价实际上带不来足够的需求，市场就会萎缩。最后，当然，如果你把价格降到零，嗯，需求就会降到零，市场规模也会归零。你知道，我想过的一个例子是，嗯，光纤通信的成本，对吧？Speaker: Jevons paradox, right? It's just a sort of narrow example of price elasticity of demand. And in in general, all markets are U-shape, right? Like so initially when the product is terrible, um there's no demand. And then the product gets better and people will pay a lot for but it's uh but they won't buy very much. And then as the price goes down, the size of the market will will will rise because, you know, you know, it becomes um uh you Jevons paradox essentially. But then you get to a point where further price decreases don't actually drive enough demand and the market shrinks and eventually, of course, if you take the price to zero, um then demand goes to the size of the market goes to zero. You know, one example that I've I've thought about is um the cost of fiber optic communications, right?

或者说，就是通信业。比如说，过去传输信息的成本特别高，人们传输的信息也很少。后来，经过一系列创新，传输信息的成本大幅下降，传输的信息量则大幅增加，市场长期大幅增长。但后来，市场停止了增长。传输信息的成本继续下降，市场却不断萎缩。如今，我想 Cogent Communications 通过旗下一个部门承载着互联网上 30% 到 40% 的 IP 流量。而 Cogent——它最大的部门甚至都不是这个——市值也就几十亿美元。这实在算不上一个很大的行业。So or just communications. Like the cost of moving information used to be super high and people used moved very little of it. And then, you know, through a whole series of innovations, the cost of um uh of moving information uh went down a lot and the amount of information moved uh went up a ton and the market grew a lot for a long time and then uh the market stopped growing. And the cost of moving information kept falling and the market kept shrinking and nowadays, you know, um Cogent Communications I think routes 30 or 40% of all IP traffic across the internet and it's through one of its divisions and Cogent, you know, and Cogent which which is like not even its biggest division Cogent Communications is like a few billion dollar market cap company. This is like just not a very big industry.

我不是说我们的智能成本会降到那么低，但你知道，杰文斯悖论是关于曲线上某个特定位置的一种论断，而这个论断未必成立。你知道，关于你的另一个问题——更多的智能能否催生对更多智能的需求——这个问题依然存在。我对此有几个想法。比如说，也许确实会……不过我一开始就说过，要回答问题，你得接触现实，对吧？所以，如果你有一个模型，让它更多地思考物理学，它当然可以提出很多有意思的想法，但最终还是需要做实验。这并不是说我们不能做实验，但现在我们受限于我们的能力，无法……Now, I'm not saying that we're going to get quite that small in terms of like intelligence, but there you know, Jevons paradox is um is a statement about a particular spot on a curve, which doesn't necessarily have to be true. And you know, you know, it remains as your other question about like can and can more intelligence create more sort of desire for intelligence? I mean, I have a couple of thoughts on that. Like what may like certainly maybe and I um but I started out by saying like you need to touch reality to like answer questions, right? And so, if you wanted to like if you had a model and it was going to think more about physics, it could certainly think lots of interesting thoughts about physics, but eventually it's going to need to do experiments. And that's not to say we couldn't also do experiments, but now we're bound by like our ability to

说话人：现实世界的事情。Speaker: real world stuff.

演讲者：亲自做实验。所以我觉得，我们即将迎来一个不可思议的时期：会有很多很酷的新发现。你已经开始看到这类情况了：比如有人写了这篇论文，又有人写了那篇论文，还有人写了另一篇论文；如果有人读过这三篇论文并认真思考，就会意识到这一点，但当时没人想到。结果却是，模型读完了所有论文，它们就会……就会……就会把这一点找出来。然后我们会……我们将会有各种各样很棒的成果由此涌现出来。但到某个时候，我们也差不多把这件事做完了。之后的重点就会变成：“嘿，你知道，有些事情是我们不知道的，我们可以换一种方式来思考它们。我们有一个假设，但模型，即使是超级聪明的模型，也未必就更擅长猜出这类事情。”Speaker: physically do experiments. And so, I think there's going to be this incredible time where we sort of there's going to be so many cool discoveries, which you're already starting to read about where like this paper was written and this paper was written and this paper was written and if somebody had read all three papers and thought about it, they would have realized this, but nobody had. But it turns out the models read all the papers and they'll they'll they'll like figure that out and there's going to be we're going to there's going to be all kinds of great stuff that falls out of that. But at some point, we're kind of kind of finished that and then it's going to be about like, "Hey, you know, there's some way to think about things that are outside of what we know and we have a hypothesis, but a model, even a super smart model, won't be better at guessing that necessarily.

我们在猜这件事上会比最聪明、最厉害的人做得更好，不过反正那个人猜对的概率也特别低。所以，它能够想出很棒、值得去做的实验点子，但这会限制我们拓展的能力。我还会想到自己使用 AI 的方式。嗯，很多方面都是。比如，先回到商品化这件事上。我已经彻底把自己使用模型的方式商品化了。说实话，我只是因为懒得自己动手做很多东西，才这么做的。我会让 GrokBot 去构建东西，而因为我不想用光所有 GrokBot wheat tokens，我就让它运行我订阅的所有东西——我订阅了各种服务，还会让它在服务器上利用这些订阅来构建东西。We'll we'll be better at guessing that than the best smartest person, but the smartest person's odds are super low anyway. So, it'll be able to come up with great ideas for experiments to do and they'll be worth doing, but that'll limit our ability to expand. The other thing I just think about is like my own use of like AI. Um a lot on a lot of levels. Like one, just to go back to the commoditization thing. I've totally commoditized the my use of models. For what it's worth, I just it's cuz I'm lazy about how I do a lot of my like building for stuff. I'll I'll tell like a GrokBot to build things and cuz I don't want to use up all my GrokBot wheat tokens, I then have it run all of them I subscribe to all the things and I have it use all this and I'll have it build using those subscriptions on like a server.

所以我现在其实都不知道自己在用什么模型了。以前我还知道哪个模型更好、哪个更差，现在已经不知道了。这一切都在后台为我优化，我只知道事情在不断推进，而且也没怎么消耗我的订阅额度。所以一切都挺好。我很好奇你怎么看这样一批公司：它们本质上是在现实世界中进行实验，并创建一个自动化闭环。这种情况正在机器人领域发生，也正在生物领域的多个地方发生，比如生物实验室、自动化湿实验室、自动化测试实验室。嗯，你对此有什么看法吗？And so I actually don't know what model I'm using anymore. Like I used to know all the which this model is better, this one is worse. I no longer know. All of this is being optimized on the back end for me and all I know is that stuff is getting built and like it's not using up my subscriptions. And so like we're good. There's this batch of companies that I'm curious what you think about which is effectively real world experimentation companies that create a loop that's automatic. There's this happening in robotics, there's this happening in several places in bio in in bio labs and you know automated wet labs, automated testing labs. Um any any commentary on like that?

说话人：这真是个特别棒的主意，对吧？就像公司的品牌一样。是啊，真是个特别棒的主意。在现实世界中把这个闭环打通，会很棒，也会加快学习过程，这确实是个好主意。不过话又说回来，速度是有上限的。我想，我反复想到的，大概是这么一种想法：这太了不起了，我从某种意义上说，特别认同AI药丸这个说法，也就是我认为这些东西能带来什么、有什么用。但与此同时，我也意识到，事情会遇到……Speaker: Super great idea, right? Like brand of company. Yeah, like super great idea. Closing the loop in the physical world is going to be great and it's going to accelerate the process of learning and this is a great idea. But again, like there is there are speed limits. I guess I'm my my sort of the thing I keep coming back to is some version of like this is amazing and I'm super in a sense like AI pill in the sense of like what I think these are things are going to do and they're useful for. But I'm but I'm also sort of cognizant of the idea that like things bump up against

演讲者：我喜欢这样跟人们说：听着，你真的很聪明，所以你一定能明白。聪明也是有局限的。Speaker: The way I like to tell this to people is I say look, you're really smart so you must understand. Being smart has limits.

发言者：聪明是有局限的，所以光靠思考来找到解决方案的能力也是有限的。因此，如果你要建一个自动化生物实验室，把它连接到计算机上，由计算机提出一个想法，然后进行实验、不断迭代，那就太棒了。但如果这个实验需要繁育老鼠，而老鼠要一年才能长大，然后死去，那这个实验就得花10。它可能是个很棒的实验，你可能会学到很多，这当然很好。但它还是需要时间。你知道，实验室的规模也有限，所以你可能有5000个很棒的想法，但最终只能挑20个。模型可能会更好。你的几率——你做这个实验的成功几率甚至可能只有1%。结果发现，这可能比普通研究人员的平均水平高出10倍，而这太了不起了。Speaker: Being smart has limits and so the ability to just think your way to solutions is limited. And so if you're going to build an an automatic biology testing laboratory and you're going to connect it to a computer and the computer is going to come up with an idea and then it's going to run an experiment and it's going to iterate. That's awesome. But if that experiment requires breeding mice and mice need a year to grow up and die, that experiment going to take 10. And it might be a great experiment, you might learn a lot and that's great. Still going to take time. And you know, the lab's only so big and so you might have 5,000 great ideas, you're going to pick 20. And you the model may be better. You your odds your odds of that experiment working might even be 1%. Turns out that might be 10 times better than the average researcher and that's amazing.

它仍然只有 1%。这并不是在唱衰。这简直太棒了。发现所有那些我们本该知道、因为它们已经在我们的语料库里却被我们遗忘的东西，真是太棒了。发现所有这些东西，嗯，还有更好地指导我们的研究，也很棒。让研究自动化、加快进度也很棒。我们会学到东西。它会推动前沿发展。但问题是，嗯，这并不是说模型会突然环顾四周，然后说：“你们漏掉了这个。你看，这里有个新世界，是你们无法理解之类的。”我不，我不认为我们会走向那种情况。看来市场，尤其是在私募市场，以及那些处于前沿、思考人工智能的投资者中，一直特别关注投资与供给相关的东西。It's still 1%. None of this is to be pessimistic. This is like super amazing. Like discovering all the things that we should know that cuz it's already in our corpus that we forgot is awesome. Discovering all the things um like and like better directing our research is awesome. Automating that research to move faster is awesome. We will learn. It will push the frontier. But the But like this is not um This is not like suddenly the model like looks looks around and says, "You guys missed it. Like here you know, here's like you know, some new world that like you guys can't comprehend or whatnot." Like it I don't I don't think that's where we're we're headed. It seems like the market has been especially in private markets and sort of the leading edge of investors thinking about AI very focused on investing in supply related stuff.

比如说，我们要如何构建并提供所有这些智能能力，而不是我姑且称之为与需求相关的东西。有人在自己的 Meta 应用之类的平台上看个性化电视。你怎么看第一批这类公司作为投资标的？Like how are we going to build and serve all the intelligence versus like what I'll call demand related stuff. Someone watching their personalized TV on their on their meta app or whatever. What do you think about that first batch of companies as investments?

说话人：非常难，对吧？我是说，我一直多少有点故意对它们一无所知，还开玩笑说过，可能因此我的处境反而更糟。但你知道，现在正有大规模的建设。而你还得试着预测推理需求，我根本没法把供给或需求预测到误差不超过好几个数量级，对吧？比如，随着智能变得更强、成本更低，人们会多用多少推理，这真的是个非常非常难的问题。同样难以预测的，还有推理能力会提升多少、成本会降低多少。即便你能设法猜出这些，接下来还有整个价值链，而你得处在瓶颈环节才能获取超额收益。最后，当然还得面对你在价值链特定环节上的实际竞争对手。Speaker: Super hard. Right? Like I mean, I've sort of been I've joked I've been willfully ignorant about them and probably I'm sort of worse off for it. But like you know, there's this big buildout. And then you're trying to forecast like inference demand and I've I can't forecast supply or demand within several orders of magnitude, right? Like this idea of like how much more inference will people use as intelligence gets better and cheaper is like a really really hard thing. And equally hard is how much better and cheaper inference is going to get. And then even if you could somehow guess at those things, then there's the whole value chain and you sort of need to be at the bottleneck in order to capture the rents. And then of course finally you get to like your actual competition with your competitors in that particular piece of value chain.

而且这件事真的特别难。我会想到自己作为消费者使用 AI 的情况。你知道，我现在越来越常做这样的事：以前会让 AI 聊天机器人跟我女儿聊天，或者跟她发消息，教她怎么做数学题；现在我会让它制作一个 3Blue1Brown 风格的视频讲解，这样她就能看一段五分钟或十分钟左右的视频，了解幂、面积、周长之类的内容。真的很酷。我觉得如果这要花 5 美元，我就不会这么做了，对吧？所以这里有个问题：价格弹性确实存在。我想表达的是，很多需求可能来自这样的想法：比如，我可以用 Muse 应用拍下自己锻炼的视频，让它给我的动作姿势提点建议，我愿意为此花很少的钱；但如果价格真的不低，我就不会这么做了。而且我对价格的敏感度恐怕比大多数人低得多。And like it's just a super hard thing. I think about my use of AI as a consumer, you know, increasingly I'm doing stuff like instead of um like having the AI chatbot like talk to my daughter like you know, or you know, text with my daughter about how to you know, do math. Now I have it make like a three blue one brown style video explainer so that you know, she can see like a nice five-minute or 10-minute video on like you know, powers or area or you know, circumference or something. It's super cool. I don't think I would do that if it was going to be $5, right? And so there's this thing where like um there's real price elasticity. The point I'm trying to make is like if a lot of demand comes from the idea that like you know, it's one thing to like I'll film myself working out and with my Muse app and it'll like show me pointers on my form and I'll do that for very little money, but I won't do that if the price point is real and I'm probably much less sensitive to the price than like most people.

所以我认为，真正的 AI 需求会对价格非常敏感。从本质上说，我觉得这在某种程度上确实限制了消费者总共愿意在这方面花的钱。So I think there's like real like AI demand is going to be really sensitive to the to the price. Which intrinsically I think does limit in some sense like the amount in total consumers are going to be willing to spend on this.

基础设施投资与资本周期Infrastructure investment and capital cycles

发言者：也许现在是时候聊聊资本周期之类的话题了。看起来，世界上最聪明的人，以及世界上很多乃至大多数最优秀的创业者，都在亲自大力投入这场供应能力扩建。你知道，昨天如果你看了 SpaceX 的那个呃投资者日活动，这基本上就是它的主题。可以说，也许有史以来最伟大的创业者，正全身心投入到这件事中，实质上是在扩建数据中心供应能力，等等。这就像是一个历史性的问题：嗯，如果这一切都是真的，而且供需方面存在这么多不确定性，人们还有其他方式可以去创建企业、打造产品、赚钱，那你觉得为什么这么多最优秀的创业者、最聪明的人，会如此全身心地投入到供应能力的扩建中？Speaker: Maybe this is a time to talk about capital cycles or something. It sure does seem like the smartest people in the world and many or most of the best entrepreneurs in the world are heavily personally invested in this supply build-out thing. You know, if you watch SpaceX's uh investor day yesterday, like this is basically the theme of it. Arguably maybe the greatest entrepreneur of all time is dedicated to effectively doing this, building out the data center supply, etc. This is just like a historical question like um if that's all true and there's so much uncertainty about supply and demand and there are other ways to go build businesses, products, make money why do you think so many of like the best entrepreneurs, the smartest people are so completely dedicating their lives to the build-out of the supply?

说话人：这是一个规模巨大、针对某项重要技术的基础设施建设项目。如果没有强烈的价格信号，也没有大量人才投身其中，这种围绕一项重要技术展开的大规模基础设施建设是不可能发生的。所以，你可能把因果关系搞反了。你知道，显然这些事情是双向作用的，但你能想象这样一种故事吗：没有什么有意思的人投身于 AI 建设，而且他们也没赚到钱，对吧？这大概就是最好的答案。嗯，说清楚一点，这是一个规模巨大、意义重大的项目。它之所以能发生，只是因为有非常聪明、非常有才华的人把人生都投入其中。他们这样做很棒。他们会赚很多钱，也理应赚很多钱。只是这件事真的非常难；从我的角度来看——当然，可能有人比我聪明，已经想明白了——但就我所知，要弄清楚该如何在这一领域投资非常困难，也很难判断你能不能从中赚到钱。Speaker: This is a huge infrastructure build-out of like a huge and important technology. And it would be impossible for there to be a huge infrastructure build-out of a huge and important technology if there weren't a strong price signal and a ton of talent going at it. So you may have your causality backwards. You know, obviously these thing things work both ways, but like could you imagine a telling of the story where nobody interesting who'd made no money was building out the AI, right? That's probably probably the best answer. Um And to be clear like this is a huge and important project. It's only happening because super smart and talented people are focusing their lives on it. It's great that they are. They will have and deserve to make lots of money. It's just very, very hard and from where I sit and there are probably people who are smarter and figured it out, but like for where I from where I sit, it is very, very hard to figure out where one would invest along this and like how like whether or not you'll you'll make any money doing it.

这并不是说那里没有很多机会和潜力。只是我决定把更多注意力放在长期均衡结果上，也就是世界最终会走向什么样子。你知道，我不想去赌哪个铁路大亨会赚钱、我们需要多少条铁路；我更想思考的是，好吧，有了铁路之后会怎样。你知道，我真希望自己有先见之明，能看出接下来会出现像 CPG 品牌这样的东西，对吧？这大致就是我一直以来尝试思考问题的方式。It's not to say that there isn't like plenty of opportunity potential there. I've just sort of decided to focus my attention more on the longer-term equilibrium results that come from what the world goes to. You know, instead of betting on like which railroad tycoon is going to make money and how many railroads we need, I'm trying to think about, okay, once you have railroads, like what, you know, that like I'd love to have the foresight to figure out like that like CPG brands come next, right? Like that's kind of the um the way I've been trying to think about it.

发言者：你能不能稍微谈谈你对资本周期的认识？还有像这样的创新周期。我很想听听你研究了这么多市场历史之后的看法。如果我们正处于其中一个周期的中段，甚至可能是有史以来最大的一个，那么历史告诉我们这些周期是如何运作的？有什么特别值得你关注的地方？Speaker: Can you riff a little bit about what you've learned about capital cycles? Like it and innovation cycles like this. I'd love your perspective on having studied so much market history. If we're in the middle of one of these, maybe the biggest one ever, what does history teach us about these cycles and how they work that's interesting to you?

发言者：你可以把当下的美国社会想象成一个正在攒钱买房的家庭，对吧？我们在盖房子，大家都觉得手头有点紧，对吧？因为所有这些资源都投入到建设庞大的 AI 基础设施上了。电工、你知道的，水管工，还有其他各种人都要去做这些工作，结果就是经济其他领域的成本更高了。嗯，毫无疑问，我们会从中获得巨大的价值。而且我们会继续推进，因为没人真正知道会怎样。我们会一直推进，直到最终做过头。一路上，我们会让人们赚很多钱，否则大家就不会继续推进。而且由于事情都有交付周期，加上这有点像赢家诅咒，你知道，最后一个停下来的人无疑会后悔。Speaker: You could think about American society right now as like a household that's like saving money for a house, right? Like we're building a house and we're all kind of feeling pinched, right? Because all these resources are dedicated to like building out like this big AI infrastructure. All these electricians and, you know, plumbers and, you know, everything else are going to do these tasks and as consequence there's higher costs elsewhere in the economy. Um and undoubtedly we're going to get a lot of value for it. And we're going to keep pushing it because no one really knows. We're going to keep pushing it until eventually like we overdo it. And along the way you're going to make people have to make tons of money because otherwise people won't keep pushing it. And because things have lead times and because it's sort of a winner's curse, you know, invariably the last person to stop will regret it.

而且这真的太难了，对吧？真的太难了。嗯，再加上这里的技术变化实在太快了。我觉得这在其他历史上的基础设施建设中，没那么明显，对吧？但你知道，做这些事情的方法一直在发生巨大的变化。我的意思是，其他人也提过这一点，我觉得这未必是错的：比如说，我们谈过模型，但即便到了芯片层面，或者其他什么层面，你知道，人工智能也有可能让英伟达之类的东西变成大宗商品，或者在各处实现递归式自我改进，比如在……And it's just so hard, right? It's just so hard. Um and then you that with the fact that there's just so much dynamism in the technology here, which I think was less true for sort of other historical like infrastructure build-outs, right? But like um you know, there's just so much change in in terms of how you do these things. You know, I mean other people have made the point which is I don't think is necessarily wrong that like even going, you know, we talked about the models, but even at like the chips level or whatever that, you know, the potential for um AI to commoditize even things like Nvidia or sort of recursive self-improvement everywhere like at the

发言人：对，到处都是。而且其中很大一部分最后都只是智能。比如英伟达，真的、确确实实就是在制造……Speaker: Yeah, like everywhere. And a lot of it ends up being like just intelligence. Like Nvidia literally quite literally like makes

发言人：他们发给台湾的那些平面文件，对吧？嗯，现在他们的平面文件很难制作。但他们有很多人会认真思考这件事，对吧？所以理论上是可以制作出来的。然后问题就变成：你要做到什么程度？如果你稍微从前沿退后一步呢？这样一来，制作成本就会大幅下降。Speaker: flat files that they send to Taiwan, right? So like Now, their flat files are very hard to make. Um but like what they have is a lot of people who think really hard about it, right? And so in theory you could make them. And also then the question becomes like at what point are you Like what if you step back a little from the frontier? Like then the cost of making something comes way down.

物理规模、智能体与定价风险Physical scale, agents and pricing risk

发言者：在那种情况下，你的判断模型会是这样吗：实体资产、实体资产和规模，大概是你想要投资支持的、处于这个等式供应端的企业所具备的特征？比如台积电？Speaker: In that world, would your model be that physical physical and scale are sort of the things that you'd want in a business that you were to back on the supply side of this equation? TSMC?

说话者：我不想把自己包装成什么台积电专家。我的意思是，他们做的事情当然有更好的特质，我觉得这一点确实如此。他们还面临着一个挺有意思的问题：他们做成了一件难度极高的事，需要多年积累起强大的工艺能力，而且极难复制。嗯，这件事也很难靠 AI 来实现，因为它太实体化了，而且涉及许多细枝末节，其中很多甚至是 AI 无法理解的，对吧？这就像是系统里的某个人，多少了解这套系统，知道必须这么做，对吧？因为需求太旺盛，而他们又一直无法满足需求，所以基本上导致全世界的人都在试图复制他们的护城河。Speaker: I don't want to carry myself around as some sort of TSMC expert. I mean certainly what they do has better attributes and I think in that regard. They've They've also got this sort of interesting problem where they did they did this super hard thing that requires like all this process power built over many years and is super hard to replicate. Um and much less amenable to like an AI because just because it's it is so physical and because it's all these little things that are many which aren't like even legible to an AI, right? It's like it's like some guy in who kind of is in the system kind of knows that you have to do this, right? Because there's so much demand and because they haven't been able to meet it, they've basically caused everyone in the world to seek to copy their moat.

这不算一个特别好的事实。嗯，不过我觉得，更广泛地说，如果把视线从 AI 建设热潮中移开，想想企业——我们聊过 Meta 的例子。嗯，关键在于，有些企业拥有实体上的邻近优势，或者实体规模，可能会从中受益：如果它们能利用智能更好地服务客户，或者因此降低成本的话。我想回过头来梳理一下智能的天然互补因素，并请你解释为什么它们与智能互补，这样大家就可以进一步思考：哪些公司具备这种特点，可能值得关注？我们来谈一个实体邻近优势的好例子。你会如何看待 DoorDash、Uber 这类企业？它们的本地密度效应非常重要。It's like a not a great fact. Um but I you know, I think more generally sort of stepping outside the AI build-out like if you think about um you know, businesses We talked about the meta example. Um just the idea of businesses that have like physical proximity or physical, you know, um scale might might benefit if they can use intelligence to serve their customers better or have like lower costs as as a consequence. I want to go back through the natural complements to intelligence and just have you explain why why it's a complement to intelligence so that people can go away and think about okay, what companies have this feature that might be interesting. Good example to go talk about physical proximity. How would you think about DoorDash or Uber or these businesses that have local density effects that matter a lot.

智能无处不在，是否会自然而然地放大这类规模赢家的优势？在这类情形中，物理上的接近似乎是某种关键优势？Does intelligence being ubiquitous sort of naturally magnify all of the scale winners of this type where physical proximity is some is some sort of key advantage?

说话人：嗯，所以如果你是某种产品的低成本生产者，我举个比较概括的例子。比如说，如果每个人都有一个 AI 智能体，那么很多决策就会交给 AI 智能体处理，因为它能帮你优化，帮你把那些你原本会漏掉的零分零角之类的小钱都捡起来。我们就拿……嗯，我觉得可以把消费者选择看作一个光谱，对吧？一端是尽可能提高我的银行存款利率。这种事我觉得几乎可以完全交由 AI 来判断。只要有 FDIC 存款保险，我要的就是最高利率。光谱的另一端可能是挑选我最喜欢的毛衣。这件事就完全得由我自己判断。Speaker: Well, so if you're the low-cost producer of something, I'll give you a sort of stylized example. So like at one If everyone has an AI agent, then what happens is many decisions get offloaded to the AI agent cuz the AI agent helps you like optimize, helps you pick up all the like pennies and dollars and nickels and stuff that you like were leaving on the ground. And so let's take the example of So So I think about it consumer choices in like a spectrum, right? Like on the one hand is like maximizing my bank rate, being deposits. Like This is something where I think I can almost entirely like defer to the AI's judgment. Like as long as it's FDIC insured, I just want the highest rate. On the other hand, spectrum end of the spectrum might be like picking my favorite sweater. This is something where it's just all of my judgment.

AI 可以帮我找一些东西供我参考，但最终你知道，还是得由我来决定。我没法把这种判断交给它。而且这中间有一个光谱。我们暂且假设，垃圾清运这件事是我可以把判断交出去的。你可能会说：“不，我真的很在意那个人，而且我信任这项服务，他们会开车到我家来，诸如此类。”但我们先把这些都放到一边。现在，如果我做的是垃圾清运生意，嗯，我可能做得很出色，但要让人们愿意换一家，还是非常难。他们大概是这样——我猜你可能根本说不出自己付了多少垃圾清运费。嗯，很多人说得出来，但我猜你说不出来。所以结果就是，你得费尽心思，才能让人们注意到这件事，愿意换一家垃圾清运公司——哪怕你提供的服务更好、价格更低。The AI can source me things to look at, but in the end, you know, I need to decide. It's going to I can't really delegate that judgment to it. And there's a spectrum. And so let's just pretend for a moment that garbage collection is something where I can defer the judgment. You might say, "No, I really care about the guy and I trust the service and they're driving to my house or whatever." But let's just set all that aside for a second. So right now, if I'm in the waste collection business, um I might be great at it, but it's still really hard to get people to bother to switch. They sort of have like I doubt you know what you pay for garbage collection like off the top of your head. Um many people do, but like I doubt you do. And so what happens is you have to do all this work to get people like pay attention to bother to like switch garbage collection companies even if you're offering a better service at a lower price.

你可以想象，就像我的代理一样，他们的代理可以跟我的代理聊，然后说：“嘿，我每个月能帮你省五十美分，你懂的。”代理就会说：“垃圾会有人来清运吗？”我就说：“会，垃圾会有人来清运。”然后，没问题，我就会换。所以，第一层影响就是你转而使用低成本供应商。但整体市场效应其实也挺有意思的。假设现在有一条街。以前，如果我这条街上的人都用同一家垃圾清运公司，对社会来说会更有效率，因为那人可以沿街开过去收垃圾，这样就能给我们所有人更低的价格，还能赚更多钱、更多钱、更多钱。但要协调我们这样做非常困难。因为我们大家就是懒得换垃圾清运公司，所以大家基本上都各用各的。嗯，你知道，那位在这条街上有两名员工的老板没法挨个给其他人打电话说：“嘿，我在这条街上有两名员工。”You could imagine that like my agent, their agent could talk to my agent and be like, "Hey, I'll save you a 50 cents, you know, a month." And the agent will say like, "Will the garbage go away?" And I'm like, "Yes, the garbage will go away." And like, no problem, and I'll change. So, the first-order effect is that you move to the low-cost provider. But the general market effects are actually sort of interesting, too, because now let's say that there's a street. And before, like, it would be more efficient for society if all of the people on my street had the same waste garbage collector, cuz the guy could drive up the street and pick up the trash, and he could offer all of us a lower price make and make and make more more money. But it's really hard for us to coordinate that. Because what happens is like all of us are just lazy about switching garbage collectors, and so we all kind of have one, and um you know, the guy with with two guys on the street can't call everyone else up and say, "Hey, I've got two guys on the street.

其他人都只有一个。我是我是低成本生产者。让我把这些整合起来。”所以，二阶效应可能是，你知道，那个在我这条街上有两两个人的家伙，可以去找街上的其他所有人说：“嘿，我给你们便宜五毛钱，你知道，垃圾清运的价格更低。”然后大家都会转过来。于是现在你就会看到这种情况：本地的，你知道，就是每个人都转向低成本生产者，而后者反过来又能成为成本更低的生产者。所以，嗯，这在某种意义上实际上会创造出更好的企业。嗯，低成本的那个人会赢得更多业务量，但利润率要低得多。所以，你知道，很多公司都有这种情况，你知道，研究它们时会发现，很多公司都有各种混合效应，指向不同的方向。Everyone else has just one. I'm I'm the low-cost producer. Let me roll this up." So, a second-order effect might be that, you know, the guy get who has two two people on my street can go to everyone else on the street and say, "Hey, I'll offer you 50 cents, you know, a lower price on the garbage." And everyone will switch. And so now you'd get this like local, you know, like everyone moves to the low-cost producer, who in turn gets to be like a lower-cost producer. So, um the that actually creates in some sense like a better like business. Um where the where the low-cost guy wins a bunch more volume at like much lower margins. Um so like, you know, a lot of companies have like, you know, have like mixed As you study them, like a lot of companies have like mixed effects that point in different directions.

嗯，呃，你知道，因为因为这些事情。Um uh you know, because because of these things.

发言人：你认为智能体会对你的世界产生什么影响？你的角色会是什么？五年后会有什么不同？比如，最好的智能体能不能像你一样，构建一个由 Carvana、Hilton Grand Vacations 和 Meta 组成的投资组合，因为你训练了它，让它学会你的思考方式？Speaker: What do you think the impact of agents is going to be on your world? What will be your role? How will it be different in 5 years? Like, will the best agent be able to build a portfolio of Carvana and Hilton Grand Vacations and Meta and in the same way that you would, cuz you trained it how you think?

说话人：更好。就我做的事情而言，对吧？比如，我认为从长远来看，我们可能会被竞争淘汰。Speaker: better. In terms of what I do, right? Like, I think that in the long term we'll probably get competed out.

发言人：定价……定价风险是你们做的事情吗？Speaker: Pricing pricing risk as the thing you do?

说话者：是的。计算机辅助国际象棋出现后，有很长一段时间，自由式国际象棋的表现更好：人类与计算机程序合作，能够胜过单独的人类或计算机。嗯，这只是因为人类和计算机的思考方式不同，而两者能够相辅相成。我认为在投资领域也是如此，有一段时间……Speaker: Yeah. When computer-aided chess came along, there was this long span of time where freestyle chess was better, where where the human working with the computer program was able to outcompete either the human or the computer by itself. Um and this is just because humans thought about things differently and computers thought about things differently and they were synergistic. And I think in investing for for a while

演讲者：我们现在正处于那个时代。Speaker: We're in that era now.

发言者：我们正处在那个时代。而这是因为，你知道，有些事情我对这个世界的理解，是模型所不具备的。嗯，这得益于我是一个人，并且亲身经历过生活。我掌握着一些关于这个世界的信息，而这些信息并不在模型中。毕竟，互联网上的信息再多，也不是所有东西都真的在网上。虽然这确实如此，而且我认为这种情况还会持续一段时间，但你知道，我能够利用模型来协助自己，再由我来作出判断，这样我们合作起来就能作出更好的判断。不过，你也可以想象未来会出现这样一种情况：这种差距逐渐缩小，直到不再重要。然后，你知道，我不认为模型能够预测未来，正如我们讨论过的，未来在某种程度上是内在地不可预测的。Speaker: We're in that era. And that's because, you know, there are things that I understand in the world in ways that the model just doesn't. Um, by virtue of being a person and having lived a life. And there's information I have about the world that just isn't in um, models cuz as much as as much information is on the internet, not everything is actually on the internet. While that's true, which is going to I think be a while, um, you know, the ability of me to use models to um, assist me and then make judgments will make better judgments working together. Um, you could you could imagine a circumstance though um, in the future where that gap diminishes to the point where it doesn't matter anymore. And then, you know, models, you know, I don't think models will be able to predict the future as we talked about like the future is in some ways very intrinsically unpredictable.

嗯，不过模型对结果的概率分布可能已经是你能做到的最好水平了，而在预测中加入一个人，就有点像在判断 7 天后会不会下雨这件事上加上我的判断。你知道，克利夫，我其实不在乎你怎么想。我用的是天气预报。就像这样，这样，这样才会更好。不管你怎么调整，只会让它变得更糟。也许这一天终会到来。那真是个非常有意思的时刻。投资涉及很多东西，对吧？比如说判断管理层之类的，对吧？但也许，也许模型能够判断管理层。它们能够采访管理层，能够弄清楚情况。然后，然后事情就变得很有意思了：你要怎么筹集资金？你会说：“你知道，我想经营一家游乐园。”模型就会说：“当然。”Um, but the model's distribution of outcomes may be um, the best that you can do and that adding a human to the forecast would be kind of like adding my judgment to whether or not it's going to rain in 7 days. You know, Cliff, I don't really care what you think. I'm I'm using the forecast. Like this is this is this is going to be better. Whatever However you fudge it, it's just going to make it worse. That day may come. It's a very interesting like moment there. Like there's a lot to investing, right? You think about like judging management or something, right? But like maybe maybe the models will be able to judge management. They'll be able to interview them. They'll be able to figure it out. And then and then it gets really interesting like how do you raise capital? You'll be like, you know, I'd like to run an amusement park and the model will say like, "Sure.

“这里有大概六个地点，开个游乐园……工作园区能运作起来的概率是51%。”而且，你知道，只要你真的非常投入就行。我评估过你，你成功的概率大概是51.2%，因为你的水平其实略低于平均线，对吧？然后你会说：“好吧，那我怎么才能做得比平均水平好呢？”我会说：“嗯，你知道吗？你有没有想过开个卡丁车场？”不过，嗯，你知道，很遗憾，我觉得这对我的职业生涯影响不会太大，因为我觉得还得过一阵子才会有影响。Here are like six locations that have a 51% chance of an amusement park work park working." And, you know, as long as you're like really committed to it. And I've assessed you and you have like a 51.2% chance of like succeeding cuz you're actually slightly below average, right? And you'll be like, "Okay, well, what can I do better than average?" And I'll say, "Well, you know what? Have you thought about like running a you know, a go-karting park?" But um, you know, unfortunately it I don't think it's going to affect uh, my career that much cuz I think it'll take a while.

发言者：那么，用什么来取代价格这一组织原则和协调经济活动的信号呢？价格是个奇迹，对吧？因为它是一个包裹着激励机制的信号。Speaker: What about replacing price as the organizing principle and signal that coordinates economic activity? Price is a miracle, right? Cuz it's a signal wrapped in an incentive.

说话者：那是什么意思？Speaker: What does that mean?

说话者：所以，价格是一种激励。它会随着价格上涨或下跌，让你想多买或少买某样东西。嗯，它也是一种信号，因为它传达了某样东西的相对价值、稀缺程度，以及生产成本。它就像一种能协调各方的奇妙机制。嗯，而且我觉得，即使拥有无限的智慧，也很难找到一个更好的系统。嗯，确实挺难的。Speaker: So, price is an is an incentive. It causes you to want to buy more or less of something because it goes up or down. Um and it's also a signal cuz it says something about the relative value of something and the scarcity of something and the cost to produce something. It's like a miracle of a mechanism to coordinate things. Um and uh I think you'd be really hard-pressed to find a better system even if even with like unlimited uh unlimited intelligence. Um it'd be pretty hard.

Sponsors: Vanta and Ridgeline

讲述者：Vanta 自动化合规工作，让团队减少安全审查上的耗时，把更多时间用于获取客户。Vanta 可将审计准备时间缩短 82%，并即时提供最新的信任证明，无需繁琐的手动操作，助您更快、更顺畅地成交。客户反馈称投资回报率达 526%，目前有超过 16,000 家公司使用 Vanta，包括 Ramp、Harvey 和 Snowflake。访问 vanta.com/invest，立即开始并享受 1,000 美元优惠。Ridgeline 是唯一一个 AI 原生的投资管理平台，可将前台、中台和后台整合到一个系统中。它为企业提供统一的数据平台，并利用 AI 智能体自动处理入职、对账、交易后合规和报告等重复性工作，让团队能将更多时间用于投资、服务客户和拓展业务。Speaker: Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant up-to-date proof of trust without all the manual work so you can close deals faster and with less friction. Customers report a 526% return on investment and more than 16,000 companies use Vanta including Ramp, Harvey, and Snowflake. Get started with $1,000 off at vanta.com/invest. Ridgeline is the only AI-native investment management platform that brings the front, middle, and back office together in one system. It gives firms one place for their data and uses AI agents to automate repetitive work like onboarding, reconciliation, post-trade compliance, and reporting so teams can spend more time on investing, serving clients, and growing the business.

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市场、资本配置与制度Markets, capital allocation and institutions

发言人：也许换一种问法，可以从投资的角度来看：如果无处不在的智能变得强大得多、成本低得多，而且在某种程度上能够复制你的工作，而你又特别擅长评估风险，会怎么样？我的朋友丹说，这大致分为三个层次。第一个层次是，我输入一个提示，然后得到一些结果。第二个层次是，我搭建一个瞭望塔。也就是说，我的直觉每天晚上都会运行一组任务，按照预定时间在后台执行。第三个层次，也是最能发挥智能作用的层次，就是它始终在线、主动行动，自行摸索如何运用自己的智能，而且会一直这样运行下去，能做多少事几乎没有限制。如果我想象第三个层次应用于一切，比如哪些东西应该如何定价、如何提升效率、如何优化，诸如此类的事情……Speaker: Maybe another way of asking the question is kind of like investing if ubiquitous intelligence gets so much better and so much cheaper and in some way um it can replicate what you do and you're really good at pricing risk. A friend of mine, Dan, was saying there's sort of three levels to this. There's I put in a prompt and I get something back. That's level one. Level two is I set up a watchtower. So, I set up a you know, my instinct every night runs like a set of tasks and it happens in the background on a schedule. And level three, which produces the most use of intelligence, is it's just always on proactive, figuring out its own way to use its own intelligence, and that just like goes on forever and it's sort of unlimited in how much it can do. If I think about that level three applying to like everything, like what things should be priced, efficiencies, optimization, all this kind of stuff.

发言人：这会让价格更有吸引力吗？Speaker: Does that add something interesting to the price?

说话人：让市场竞争更加充分，对吧？价格有很强的黏性，对吧？这是一个众所周知的宏观经济现象，也就是价格黏性。很多东西的定价都挺随意的。想想存款之类的。我是说，外面的竞争确实非常激烈，但实际上并没有理论上可能达到的那么激烈。所以我觉得，如果每个人都有代理替自己办事，并且不断进行某种优化，那么垃圾收运的价格就会变动得更多。随着垃圾收运的各项投入成本不断变化，每家垃圾收运公司都会不断重新核算财务数据，试图算出自己的真实边际成本，然后向每个客户报价，只要服务价格能高于边际成本就接单；而每个代理都会转向成本最低的服务商。Speaker: Sharpens markets, right? There's a lot of like stickiness to prices, right? It's a well-known macroeconomic phenomenon. It's like price stickiness. There's a lot of like lazily priced stuff. Think about deposits and such. I mean, it's a really competitive world out there, but it's actually not as competitive as in theory like it could be. So, I think you know, if everyone has agents who are working on their behalf and constantly sort of optimizing, I think you know, the price of garbage collection will move around a lot more. As all the input costs of garbage collection evolve, and every you know, a garbage collecting company is constantly like re-running its financials and trying to figure out its true marginal cost and bid every customer where it could like serve for like you know, more than marginal cost or you know, and every agent is switching to the lowest cost provider.

这就很像垃圾清运的价格：你不会连续12个月每月只付60美元，然后等着那个人回来告诉你涨价5%，明年要付63美元，而你甚至都没注意到；你只会觉得，嗯，他们不过就是来收垃圾的。你的垃圾收运价格会因日期而异，你知道，有一周可能是某个时间……我想，价格会变得很有意思，也会更准确、变动得更快。That's just a much like the price of garbage you won't just pay $60 a month for 12 months and then have the guy come back to you and say it's a 5% increase, it's going to be 63 next year, and you don't even notice and you just like you know, like it's they're just picking up your garbage. Your your garbage collection will vary by day and you know, and one week it'll be whatever time it there'll be all this interesting much more accurate, much more rapidly moving prices, I would imagine.

发言人：你在资本市场创新方面有没有看到什么有意思的东西？《经济学人》有篇很棒的报道，你刚才提到了美国资本市场这个庞然大物，以及它在适应新事物、创造新工具和新的融资方式方面有多么出色。这一直以来都……Speaker: Have you seen anything interesting in capital markets innovation? There there was this awesome economist story you're talking about the behemoth that is American capital markets and how good it is at adapting to new stuff and creating new new instruments, new ways of financing things. And that's been

发言人：这和那些通常会引发……的技术创新一样，都是美国故事的一部分。换个角度来说，华尔街和硅谷之间存在着一种奇妙的相互关系。对你来说，市场、资本市场、融资方式之类的方面，有什么有意思的趋势正在酝酿吗？Speaker: as much a part of the American story as the technology innovations that typically happen to cause, you know, like that there's this amazing interrelationship between Wall Street and Silicon Valley would be one way of thinking about it. Anything interesting percolating for you in in markets, in capital markets, in ways of financing things, things like this?

发言者：嗯，我的意思是，我只是觉得，停下来片刻，也许该为我们资本市场在 AI 方面所做的事情祝贺一下自己。你知道，人们曾担心美国活力衰退。我想我们刚刚把什么来着？GDP 的 3% 重新调配了，对吧？几乎转眼之间，就投入了这个高度投机性的领域。这真是件了不起的事。当然，谁知道呢？也许应该是 2%，也许应该是 6%，对吧？但我们能以这样的规模、这么快地重新调配资源，确实令人惊叹。世界上没有其他地方能做到这一点。而且你知道，无法调动投资和生产性资源，可能会成为生产率增长的硬性约束，尤其是在技术快速进步的情况下。Speaker: Well, I mean, I just it's fun to take a moment to pause and maybe congratulate ourselves as as a as capital markets for what we're doing with AI. Like this I mean, how you know, people were worried about this, the decline in American dynamism. I think we just like redeployed what is it? 3% of GDP, right? Like right into this like highly speculative venture basically on the on the turn of a dime. Um it's just this remarkable thing. Of course, who knows? Maybe it should be two, maybe it should be six, right? But it is amazing that we can redeploy resources at this scale um this quickly. And there's no one else in the world that's been able to do it. And um you know, certainly the inability to move investment and productive resources um can be a binding constraint on productivity growth even with especially with rapid technological progress.

所以，嗯，你知道，至少就资本而言——显然，你知道，我们会谈到资本，但资本投资是和人们做各种不同的事情联系在一起的。比如今天美国人就在做各种不同的事情。比如每天，人们醒来后就去某个地方，我不知道，比如宾夕法尼亚州，去建一座数据中心，对吧？要不是有我们这整套基础设施，能够以某种方式调动如此庞大的资本，这些事情根本不会发生。这太不可思议了。至于深层创新，我觉得，嗯，人们愿意为投机性项目承担风险，并以不同方式分割利益，这个基本理念大概是不可约减的。我想，随着时间推移，人们会为本质上相同的事物想出许多不同的名称。And so um you know, at least with regards to capital and obviously you know, we talk about capital, but the capital investments are tied to people doing different things. Like Americans are doing different things today. Like every day, like people are waking up and going to some place in I don't know, Pennsylvania and like building a data center, right? And like that wouldn't be happening if it wasn't for this whole infrastructure we have that's somehow been able to move this enormous amount of capital. It's incredible. As like deep innovation, I don't I think the fundamental idea that like people are taking risk on speculative ventures and sort of slicing up the pie in different ways is sort of irreducible. I think people will come up with lots of different names for what are essentially the same things over time.

我不觉得这有什么本质上的不同。嗯，不过这确实令人难以置信。我还应该提一下，我敢肯定，等潮水退去的时候，我们最终会发现各种各样的欺诈、不良行为之类的。但考虑到这件事的规模，我猜它做得实在太干净了，真是不可思议。比如你要是读读铁路建设的历史什么的，就会发现其中有多少浪费、欺诈之类的问题。总会有一些，但这件事不一样：有很多人基本上都在努力做正确的事，而那些治理方面的机制，我们似乎都已经习以为常了。这真是不可思议。我觉得，美国的资本市场，以及总体上的资本市场，都被严重低估了。想想看，任何人都可以拿出十万美元买股票，获得一篮子由管理良好、动力充足的团队组成的投资组合，这些团队正在建设各自的公司，而且基本上都在做正确的事。你可以几乎即时、几乎零成本地完成这一切，然后等不需要的时候再卖掉。I don't think that's like deeply different. Um but it is incredible. I should also note that like I'm sure we'll discover all kinds of like fraud and bad behavior and stuff eventually like when when like the water goes out, but I think given the magnitude of this, I suspect like it's so clean. Like it's amazing. Like if you read about like the railroad build-out or something, like the amount of you know, like waste and fraud and like whatever. Invariably there is some, but like the like this is like there's a lot of people trying to basically do the right thing here and like the governance parts that seem to kind of we just take for granted. Like it's incredible. Like America's capital markets and the capital markets in general I think are just like incredibly under appreciated. Like the idea that you know, you know, the any person can sort of buy $100,000 of stock and get, you know, a portfolio of well-run, you know, well-motivated teams, you know, sort of building the companies that are they're sort of basically doing mostly the right things and do it basically instantly and at basically no cost and then sell it as soon as they're done.

就像，你知道，这很了不起。而且，所有那些公司以及人们投资的其他方式，竟然能在总体上相当好地配置社会资源，这简直……我是说，你懂的，实在令人难以置信。整个系统都被严重低估了。Like you know, is amazing and then the fact that all those companies and other ways people invest like can then allocate society's resources like pretty well like in the scheme of things is just I mean, you know, just incredible. Like the whole system is just very underrated.

发言人：你认为，为什么美国在这方面如此占据主导地位？Speaker: Why has America so dominated this, do you think?

说话人：我个人的猜想是——每个人都有自己的理论，嗯——这在某种程度上是遗传、文化演变和制度演变之间相互作用的结果。简单来说，来到美国的人是那些最愿意冒险的人，因为前往美国本身就非常冒险。我认为，嗯，冒险倾向在很大程度上受遗传因素影响。所以我觉得，这种倾向在美国人当中更常见一些。当你身处一个更推崇冒险的地方，还会由此衍生出许多其他东西。比如说，冒险更为常见，继而会逐渐形成某些文化和制度方面的因素，进一步强化这种倾向。人们普遍认为，冒险可能会失败，这也是可以接受的。Speaker: My pet theory and everyone has their own theories, um, it is some sort of interplay between genetics, cultural evolution, and, um, institutional evolution. So basically like the people who came to the United States were the subset of people who were most interested in taking risk because taking going to the United States was very risky. And I think that um, risk taking has a meaningful like genetic component. And so I think that tendency is just more common amongst, you know, Americans. When you're in a place where um, risk taking is more the norm, there's also a whole bunch of like other things that come from that. Like um, it's it's more the norm and then that leads to build-ups of sort of cultural and institutional things that reinforce that. It's understood that people can take risk and fail.

这就会带来合理的破产法，也会带来相应的劳动法，以及支持这一切的风险投资。所有这些都会彼此强化。嗯，接着就会出现成功案例，而我们……所以，你会得到这么一整套东西。嗯，不过这只是我个人的理论。That leads to bankruptcy laws that make sense. That leads to labor laws that support that. That leads to you know, venture capital that supports that. And all of those things reinforce that. Um and then that leads to success stories which we So you get kind of this whole soup. Um but that's my pet theory.

说话者：所以，如果未来某个时候建立了月球基地，你会看好那个社区在经济上取得巨大成功的能力。Speaker: So if there's a moon base at some point you're going to be bullish on that community's ability to do really well economically.

说话人：是的，确实，这么说很贴切。美国一直以来都非常有创新精神，创新能力也令人惊叹。所以，如果月球上建了基地，我也不会惊讶那是我们的。尽管中国人聪明又众多，嗯，欧洲人也存在。Speaker: Yeah, actually that's a great that's a great way to put it. The United States has has been persistently like innovative in in like a very amazing way. And so if there's a moon base I wouldn't be shocked if it's ours. Even though the Chinese are brilliant and numerous um and the Europeans exist.

发言者：当你看到利率上涨时，比如你看到那张很棒的长期利率走势图——比如美国的10年期利率。它基本上一直都是这个样子，几乎贯穿了我们的一生。我当时正想，呃，评论一下那张图表。Speaker: When you see rates go up like the you see that great long-term chart of rates let's say the 10-year rate in the US. And it just looks like this for forever basically our whole lives and I was going uh comment on that chart.

发言者：嗯，我的意思是，这里面的内容太多了，而且我当然不是宏观经济学专家，也不会花太多时间去思考这些，不过我可以说几点观察。比如说，你知道，其中一点显而易见的是，一般来说，当一个社会——比如说，你身处一个社会，而这个社会正在建设、发展——这就意味着你在对未来进行巨额投资。也就是说，你今天就得少消费一些。从根本上讲，所有投入到这场 AI 建设浪潮中的资源……Speaker: Well, I mean there's so much and I'm certainly no macroeconomics expert nor do I spend much too much time thinking about it but I'll make a couple of observations. Like you know, one obviously is just you know, in general when society has like you're you're society and you're you're building it you're making a giant investment in the future. Means you're going to have to consume less today. Like fundamentally all of the resources going into this AI buildout

发言者：是关于未来的。Speaker: are about the future.

说话者：它们关乎未来，本质上就像那个水管工：他今天建造的东西并不是供今天消费的。他本可以创造当下的消费品，但他却在建造一座数据中心，而那是供未来消费的。所以，在这个非常复杂的谜题中，有一部分我思考过：利率之所以更高，是因为我们在鼓励人们为未来储蓄。嗯，而做到这一点的方式就是付钱给他们。Speaker: Are about the future and they're and fundamentally like the plumber building that today is not making consumption today. Like he could be making today's present consumption. Instead he's making a data center which is future consumption. So one piece of a very complex puzzle that I have thought a bit about is this idea that rates are higher cuz we're inducing people to save for the future. Um and the way you do that is you pay them.

说话人：简单。Speaker: Simple.

公司内部的 AI 应用红黑大战AI adoption inside companies

嘉宾：如果我不稍微问问你的投资组合，大家肯定会骂死我。这对我来说太有意思了。大概是80%的Carvana、度假业务，5%的Meta。基本上就是你的投资组合。不过粗略来说，里面大概就五项。没错。其中一项就占了80%。那么，想想你的投资组合，以及那些公司——显然你对它们非常了解，而且你曾建议它们如何考虑使用……Speaker: People would kill me if I didn't ask about your portfolio a little bit which is uh so interesting to me. It's like 80% Carvana, vacations, 5% Meta. It's basically your portfolio. But like rough justice it's like five things. Sure. And one thing is 80%. And so if you think about your portfolio um and those companies which obviously you know extremely well and you were advising them on how to think about using

说话者：是的。Speaker: Yeah.

发言者：关于利用 AI 转型企业的演讲，会是什么样的？Speaker: AI to transform their businesses, what's that speech look like?

说话者：嗯，首先要确保每个人都在进行氛围编程。从董事会和 CEO 开始，基本上每个人都得开始做点什么，你知道，他们得办黑客马拉松。当然，我把这个想法带到了 Carvana，他们说：“对，我们管这叫披萨派对。”我说的可不只是开发人员，我是说每个人都得参加黑客马拉松。嗯，这些黑客马拉松未必会产出特别有用的软件，但它们能让大家知道有什么事情是做得到的。我觉得这会改变他们对可能性的预期。嗯，然后还有一件事，我告诉他们的第二件事是，CE- CEO 基本上需要转向 IT 负责人，说类似这样的话：“所以，我希望我的 Grok bot、MetaMuse、智能体之类的东西，能够以只读方式访问公司里的每一个系统。”Speaker: Well, it starts by making sure that everyone is vibe coding. Starting at the board and the CEO and like everybody needs to start building some, you know, they need to have um hackathons. And of course I brought this to Carvana and they said, "Yeah, we call them pizza parties." And I don't mean like the developers, I mean like everybody needs to have like hackathons. Um and it's not so much that you're going to build hugely useful software necessarily that comes out of these hackathons, but it's that you're going to teach everybody what can be done. And that's going to change their expectations, I think, for what can be done. Um and then the other thing, the second thing which I tell them is the CE- CEO needs to basically turn to the head of like IT and say something to the effect of "So, I want um my Grok bot, MetaMuse, agent, whatever to be able to access every system in the company on a read-only basis.

“所有我能查到的东西，比如说，如果我问某个人，我希望能通过一个提示词获取这些信息。”然后那家伙会说：“这真的很难。我们在这方面有数不清的问题。”接着他说：“这正是我的意思。我想要的就是这个。”你知道，这需要一个很大的流程，而且得一个系统一个系统地来，诸如此类。不过，如果你能做到这一点，那么你就能获得只读访问权限。Everything I could look up like if I like asked somebody I want to be able to get that in a prompt." And the guy's going to say, "That's really hard. We have like umpteen trillion problems with that." And he's going to say, "That's my point. Like I want that." And you know, that's a big process and it's system by system and all the rest, but if you can get to that um then that gets you like read-only access.

说话人：起始行。Speaker: starting line.

说话人：是的，先从起点开始。这样你就能获得只读权限。嗯，接下来顺理成章的就是 CEO 或其他相关人员需要在各处使用它，然后大家迟早都会眼红，你就可以说：“哦，你也可以用，不过我们得理清你的权限设置。”Speaker: Yeah, to the starting line. That gets you read-only access. Um and then the next logical thing is the CEO or whatever needs to like use this everywhere and everyone once is going to get jealous and you're going to say, "Oh, you can have it, too, but we need to work out your um permissions."

说话人：然后，理论上这会逐层传递下来，这样你就获得了只读访问权限。接下来总会有人说类似这样的话：“我真的很想把数据写入系统。”你就会说：“太好了。我们得弄清楚写入权限。”然后又会有人说：“我很想改改系统，因为我们刚做了这个氛围编程的东西，而且我有信心我们能做到。”你就会说：“这也太好了。我们得弄清楚要通过什么流程来管理对系统的更改。你知道，我刚才描述的东西，当然，看起来就四个要点，但它其实是一个……Speaker: And then that in theory percolates down and now you've got permission to read-only access and then invariably someone is going to say something like, "I'd really like to put data into the system." And you're going to say, "That's fantastic. We got to work out write permissions. Um and then someone's going to say, 'I'd love to change the system cuz we just this vibe coding thing and I'm confident we could do it.'" And you're going to say, "That's also fantastic. We need to work out, you know, a process by which we're going to manage changes to the system. And, you know, what I just described like sure, it's like four bullet points, but it's like that's like a

演讲者：多年工作成果。Speaker: Years of work.

说话人：这简直难得不可思议。所以，问题是，你所在的行业是否允许你有足够的时间做到这一点，而不是被那些从跳球开始的人、从一开始就领先的其他人超过。没错。所以，拥有一家非常出色的企业是件好事。Speaker: It's like impossibly hard. So, you know, the question is can are you in a business where you have time for that to happen before someone who starts for the jump ball, someone else that starts from go beats you. Exactly. And so it's it's good to have a like a um you know, a really good business.

发言者：看起来由此得出的结论之一是，现在是创办公司的最佳时机：一方面，资本市场的状况意味着人们愿意以相当低的资本成本，为高度实验性的项目提供资金；另一方面，借助这些工具，如果你从零开始、没有包袱，就能走得更远、发展得更快。你觉得这个判断大体上对吗？Speaker: It seems like one of the conclusions from that is it's the best time ever to start companies both because of capital market conditions that people will fund at fairly low cost of capital highly experimental things, but also just because with these tools if you start without the baggage, you can go so far so fast. You think that's roughly right?

说话人：我是说，没错，现在是创办公司的最佳时机——至少在明天之前都是，对吧？所以我同意，太棒了。我也想说说我的看法。你知道，跟人打交道真是件头疼事，对吧？比如说，如果你在经营一家公司，人可真够让人头疼的。现在有了这些智能体之类的东西，你就能用少得多的人做多得多的事。当然，你知道，一直以来的挑战之一就是：如果某件事你能轻松做到，那其他人也能轻松做到。所以，你打造的东西必须有某种切入点，能够创造持久价值。在这个所有智能体都乐于每天更换服务提供商的世界里，你能不能形成某种锁定效应，或者积累专有数据？究竟是什么能让你拥有持久的经济优势？Speaker: I mean, yeah, it's the best time to start a company until tomorrow, right? So, I agree. It's amazing. I'd also just say that like just thinking from my perspective, right? Like you know, people are such a pain in the butt, right? Like as a if you're running a company like they're such a pain in the butt. And the idea that you have all these agents and stuff it makes it so you can do so much more like with so many fewer people. Of course, you know, one of the challenges always is like if you can sort of easily do something, then like so can everybody else. So, there has to be some wedge by which thing you build um produces like enduring value. Does it have a path where you end up with some sort of like block in or proprietary data in this world where all the agents are happily you know, changing providers you know, every day like what gives you any enduring um economics.

但接下来当然还有更进一步的问题，对吧？就是，你知道，显然，如果你做的事情看起来有一条显而易见的路，能带来持久的经济效益，那你就不该这么做，因为所有人都在试图这么做。所以竞争非常激烈，而真正了不起的成果，往往是那些看起来毫无道理的事。你根本不该去做，但后来结果却证明确实有一条路，或者世界发生了转变。做。But then of course then there's the further thing, right? Is it you know, obviously if you if you do the thing where it seems like there's an obvious path to enduring economics, then you that's a bad thing to do because everyone is trying to do that. So, it's very competitive and the really great outcomes are the ones that make no sense. You shouldn't do it at all, but then it turns out there is a path or the world turns. do

竞争、利润率与收入分配Competition, margins and income distribution

说话人：那么，哪些是最被低估的未来竞争优势来源？如果归根结底，优秀的企业需要可持续的优势，也就是可持续的竞争优势，才能获得丰厚利润。那么，有什么是被低估的吗？我们聊过注意力、数据和物理上的接近。这些都是一些例子：由于智能变得廉价，它们未来的价值可能会远高于现在。还有没有其他类型的优势？可能是监管俘获。嗯，你觉得还有哪些类型的优势被低估了？Speaker: are the most underrated sources of future competitive advantage then? Like if if ultimately good businesses need sustainable edge sustainable competitive advantage to have great profits. Anything underrated? Underrated. We talked about attention and data and physical proximity. These are examples of things that might be super or more valuable in the future than they are today because of cheap intelligence. Is there any type of it maybe it's regulatory capture. Um are there any types of edge that you think are underrated?

发言人：我感觉这里面很多因素的权重都分配得挺恰当的。嗯，你知道，我想我有个挺有意思的问题……其实有两个问题。第一个是，这会影响利润率吗？比如说，如果从整体上看企业利润率，它们会提高还是降低？Speaker: I get the sense that a lot of these are appropriately weighted. Um you know, I guess one interesting question I've had I'll I'll two of these I was like one is does this make margins like if you think of corporate margins in aggregate or do they go up or down?

说话者：像这样说。Speaker: to say like this.

发言者：对，它们是上升还是下降？你知道，我觉得我们讨论的这些事情，似乎都指向下降，对吧？呃，有很多企业，你知道，他们之所以能获得利润，是因为Speaker: Right, do they go up or down? And you know, I think everything we're talking about would sort of argue down, right? Uh there's many businesses where you know, you know, they're making margin because

发言者：更多的消费者剩余。Speaker: More consumer surplus.

发言人：更多的消费者剩余。Speaker: More consumer surplus.

说话者：无处不在。Speaker: Everywhere.

发言者：有意思的是，如果你回头读读互联网兴起时，也就是90年代的许多研究报告，就会发现他们说的也是同一件事。嗯，所以这里的基准概率可能不太高。我们也许是错的，但我认为他们经常出错的主要原因，可能是范·汤普森所说的聚合经济学，对吧？所以也许没有这一点，预测就不会那么离谱。我确实认为，很多聚合理论会失效，因为你知道，它基本上依赖于这样一个想法：消费者只看 Google 上的第一个结果，对吧？但事实证明，如果你的智能体会搜索并查看所有结果，那么排名第一就不重要了。嗯，你知道，质量、价格以及服务等所有核心因素就会变得更加重要。Speaker: What's interesting is if you go back and read a lot of the research reports from the '90s when the internet was coming along. They said they said the same thing. Um so the base rate here might not be great. Like maybe we're wrong, but I think the main reason they were often wrong was like was this aggregation economics of Van Thompson aggregation economics, right? So maybe absent that maybe it wouldn't have been so wrong. And I do think that like a lot of aggregation theory breaks down when you know, cuz that basically relies on the idea that consumers only look at the first thing on Google, right? But it turns out if your agent looks at everything on on search then being ranked first doesn't matter and um you know, all the sort of core things like quality and price and everything else um service become more salient.

嗯，不过你知道，还有一点是，这可能会带来另一种下游效应：你将能够打造更复杂的企业，对吧？所以说，现在很多行业的结构都更偏横向，因为经营好自己这一块的复杂度非常高，嗯，这就让向上或向下拓展变得很困难，也容易造成过度复杂。借助 AI 智能体，以及基本上廉价且无处不在的代码和智能，你就可以构建完整的垂直产业链。我觉得其他国家已经有人在尝试这么做了。比如，你可以打造一家全链条的宠物护理企业，从生产宠物食品，到提供宠物保险、经营宠物医院和宠物诊所，再到所有这些相关业务。过去，这些通常分属多个层级；但现在，你可以通过让 AI 来解决协调问题、减少委托代理问题，也许这类企业就能成为现实。到最后，这些企业的利润率也许会更高，并且能带来更高的效率和更多的消费者剩余。Um but you know, the other thing is that maybe like another sort of downstream effect of this is that it'll you'll be able to build more complicated businesses, right? So so like right now a lot of industries are more horizontal in structure because the complexity of running your slice is very high um and that you know, makes it hard to do what's above and below you and not have undue complexity. With um AI agents and basically cheap and ubiquitous code and intelligence like you could build the whole vertical stack. People trying to do this I think in other countries but like you can have a full stack pet care business that does everything from make pet food all the way up through pet insurance and pet hospitals and pet vets and pet and all these things that historically were multiple layers but now because you can solve the coordination and reduce the principal agent problems by having um by having AI do it like maybe you those sorts of businesses are possible and then maybe those businesses are are higher margin in in the end and more instead of more efficient and more consumer surplus.

你知道，Carvana 算是一个例子：它是在这件事还没变得容易之前就做起来的。不过，也许人们可以把它应用到其他垂直领域，这种情况下利润率可能会上升，消费者也能获得剩余。嗯，这里另一个有意思的话题是，这会如何影响财富和收入分配，对吧？我可以从两个角度来讲。你知道，一方面，我觉得很多人都谈过这样一种观点：它会让收入分配更加集中，对吧？有这么多人靠各种实验室之类的东西，还有数据、英伟达什么的赚了钱；而且，人们认为这会让小群体获得递增收益。嗯，但反方的论点是，如果我们拥有廉价且无处不在的智能，就会发生几件事。其一，经济中有很多攫取租金的人。You know, Carvana is sort of an example of that built before this was you know easy to do but maybe people can do it in other verticals in which case maybe margins go up and there's consumer surplus. Um The other one is the fun one of things here is just how this affects like wealth and income distribution, right? I can tell tell two stories you know on the one hand I think a lot of people have talked about this idea that it concentrates income distribution, right? You've got all these people who made money on like various labs and like whatever data Nvidia and um this idea that there are like increasing returns to small groups of people. Um but the counter argument is if we have cheap and ubiquitous intelligence um a couple of things happen. One is there's all these rent takers in the economy.

你可以想象，你是一家向保龄球馆收费的软件公司，收费方式大概就是那样，对吧？然后那些人全都被压榨得很惨，这对他们来说很糟，但确实降低了打保龄球的成本。所以你知道，这就提高了所有打保龄球的人的生活水平，对吧？嗯，这是一种拉平效应，因为我在保龄球上的支出只占收入的一小部分，但收入较低的人花在保龄球上的钱占收入的比例更高。所以这对收入有一种拉平效应。如果这些零零碎碎的租金收入都消失了，企业利润就会下降——普通美国人给美国银行的钱不再流向美国银行，资本家会受损，而你知道，普通消费者会受益，对吧？这就是拉平。另一部分是，还有一整个专业人士阶层，对吧？Think you're the software company that charges bowling alleys like you know whatever, right? And those guys all get smoked which sucks for them but it does lower the cost of bowling. And so then you know that increases living standards for everyone who bowls, right? Um and that's flattening because the my my spending on bowling is small proportion to my income but like people who make less money spend more proportion to their income on on bowling. And so the it's it's a flattening effect on on income. If like all these like little rent pieces like go away the decline in corporate profits like all the money that the average American is giving to Bank of America goes from away from Bank of America the capitalists lose you know like the average consumer wins, right? So that's leveling. The other piece is like there's a whole professional class, right?

基本上，你可以把他们的角色定义为……Who basically you could define their role as

发言人：情报。Speaker: Intelligence.

说话人：智能，对吧？他们获取大量信息，并以一种相当直接的方式，将这些信息应用到特定情境中。嗯，如果这些都变成了大宗商品，第一，每个人都能获得最好的、无处不在的服务，这对所有人都有好处；第二，嗯，这对那些现在负担不起最好服务，甚至任何服务的人尤其有利。嗯，但第三，那些人之所以能赚取额外收益，是因为他们更聪明。之后，他们就只能退而求其次。呃，理论上，这或许会拉平曲线。现在，对此的反驳可能是：嗯，没错，但主动性、雄心和聪明才智所带来的回报可能会提高，因为现在我能做的事情多得多了，毕竟这些模型就像是给大脑用的自行车，而且还加了涡轮增压，对吧？Speaker: Intelligence, right? They take big corpuses of information and apply them to certain circumstances in kind of a fairly like, you know, straightforward way. Um, and if those all get commoditized, one, everyone gets the best ubiquitous, which is one good for everybody, but two, um, particularly good for people who can't afford the best right now or any. Um, but then three, those individuals, they earned these extra rents because they were smarter. They then have to drop to their next best alternative. Uh, and in theory then that might flatten the curve. Now, the counterargument to that might be something like, well, yes, but returns to initiative and ambition and intelligence could go up because now I can do so much more because these models are like a um, you know, like a bicycle for the mind on steroids, right?

就像他们赋予那些聪明、有主动性的人更多实现目标的能力。也许这实际上会让这部分人获得越来越多的回报，并让曲线变得更陡。杜鲁门曾想要一位只有一只手的经济学家。我嘛，你知道，我好像每件事都明显有两种看法。不过，呃，Like they give people who are smart and have initiative like the ability to accomplish, so much more. Maybe that actually causes like increasing returns to that subset of people and it steepens um, the curve. Truman asked for a one-handed economist. I'm, you know, I see I seem to very distinctly have two hands on everything. Um, but uh,

发言者：你知道，我觉得这是个很有意思的大问题。Speaker: you know, I think that's a big interesting question.

说话人：这些问题非常有意思。我发现，我关注的很多情况都存在许多相互交织的趋势。就像我们一开始谈到的 AI 建设热潮，一方面，我能看到 AI 有各种令人惊叹的用法，而且我已经用了大量大量的 token，也看得出自己还能再用上更多；显然，我处在分布的极右端。要是人人都像我这样用那么多 token，供应就会严重短缺。这会成为一场持续很久、规模庞大的浪潮，而所有参与建设的人都会赚得盆满钵满，对吧？但另一方面，我每个 token 的收益正在飙升，而事实证明，在很多方面，我都是个相当另类的人；人们适应起来会慢得多，每个 token 的成本也会大幅下降。Speaker: They're super interesting questions and like I've found that a lot of situations I look at like the there's a lot of cross currents. Like on the one hand everything we started the conversation with the AI buildout. On the one hand, I can see all these incredible ways to use AI and I use a good gajillion tokens and I can see how I could use a gajillion more and obviously I am in the far right end of the distribution. If everyone uses as many tokens as me, we are so short. Like it's going to be this huge thing that's going on forever and everyone and everyone who's in the buildout is going to make a fortune, right? On the other hand, right? The amount I get per token is like going to the moon and it turns out I'm a pretty unusual guy in a lot of senses and people are going to adapt more slowly and the cost per token is going to collapse.

所以也许实际上，呃，我们是不是做得太过头了？而且我可以讲两种说法，语气都一样笃定。And so maybe actually, uh, like we're totally overbuilt, right? And like I can tell I can tell both stories like with equal certainty.

投资组合构建与股权集中持有Portfolio construction and concentrated ownership

发言者：我有一位很要好的朋友，最近一直在做一个很有意思的实验。他把投资拆解为两个部分：选择持有什么，以及每种东西持有多少——这种说法过于简化了，但作为一种思考方式还挺有意思。他的实验是，他继续挑选要持有的标的，但随后分别用两种方式构建自己的投资组合：一边沿用过去的做法，另一边则把这项工作完全交给他自己搭建的一个 AI 工具，让它“负责构建整个投资组合，但只能用我选好的那些标的”。到目前为止，他发现，AI 构建的组合表现大幅优于他自己的版本，而他自己的组合构建方式本身已经相当成熟。他会提出一个简单的论点：AI 确实很擅长优化，而且它们读过所有关于投资组合构建、下注规模、凯利准则之类的论文，什么都懂。也许未来我们会转变思路：投资者的工作只是决定持有什么，而不必决定每种东西持有多少。Speaker: I have a good friend who has been running this very cool experiment where if you break investing down into picking what to own and then how much to own of each thing that you've picked overly simplified but kind of interesting interesting way of thinking about it that he's been running this experiment where he continues to pick the things to own but then runs his own portfolio construction as he would have in the past in in one lane and in the second lane is just completely handing that off to an AI harness thing that he's built and says, "You do all the portfolio construction but stick with the stuff that I've picked." And what he's found so far is that is drastically outperforming his version of portfolio construction which itself is quite sophisticated. And the simple argument that he would make is well, AIs are just really good at optimization and they've read every paper on portfolio construction and all this kind of stuff and bet sizing and Kelly criterion and like they know it all and that in the future perhaps we make that transition where the investor's job is just picking what to own and not how much of each to own.

我想问你，你怎么看这个想法和未来？因为你的投资组合非常特别，80%都集中在某一种资产上。我想，如果你处在第二条赛道，AI为你的投资组合分配的权重会和你自己的选择大不相同。And I ask you this question what you think about this idea and the future because you have such an unusual portfolio where again 80% of it is in one specific asset and I guess is if you were in lane two your portfolio would the AI would weight your portfolio very differently than you have chosen to do it.

说话者：我体重过轻。Speaker: I'm underweight.

说话者：所以我真的特别想知道你对这个想法有什么看法。Speaker: So I'm just I'm dying to know what you think about about this idea.

说话者：你知道，Speaker: You know,

发言人：低配 Carvana。应该是90。Speaker: underweight Carvana. Should be 90.

说话人：时间尺度可能也很值得思考。嗯，你知道，我在想 AI 将如何侵入这个行业时，我觉得它会从较短的时间尺度开始，然后逐步延伸到较长的时间尺度。所以，嗯，你知道，因为挑战之一是，这里没有验证闭环。比如说，你朋友之所以能这么说，是基于……我的意思是，肯定不是基于 20 年的业绩。我非常怀疑 AI 进军市场的步伐会是这样——我的意思是，它们已经开始了，对吧？我不知道 Renaissance Technologies 是怎么做到的，但我猜……Speaker: Time time scales probably are interesting on that to think about too. Um, you know, as I think about how AI is going to encroach into this business I think it's going to start at shorter time scales and work its way up to longer time scales. And so be be you know, cuz one of the challenges is that like the there's no verification loop. Like your your friend's ability to say that is based on results over I mean, certainly not 20-year results. I strongly suspect that like the march of AI into markets and I mean, they're started, right? Like, I don't know how Renaissance Technologies did it, but like I assume

说话者：看起来他们可能比所有人都更早发现了变压器。我可不是在开玩笑。Speaker: Seems like they might have discovered a transformer before everybody else. It's like I'm not kidding.

发言者：如果有人从秒级开始，逐步扩展到分钟、小时、周、月这样的较短投资周期，我不会觉得意外，他们会把越来越多的事情交给模型来处理、委托给模型。这完全说得通，因为你完全可以设想把它分成两部分：一部分是设法丰富模型。我想，已经有人在做类似的事情了，这只不过是更复杂的版本。你可以采用一种完全量化的策略，然后加入你认为属于定性、由人设计的分析；接着还可以再用一种量化策略，把这些定性分析纳入其中。我想，许多多策略基金都会做这类事情。而他所做的，大致就是其中一种稍有不同风格的做法。Speaker: It wouldn't shock me if people who invest over shorter time scales starting with with seconds and working their way out to minutes, hours, weeks, months find themselves handing more and more delegating more and more to models. Makes perfect sense in that you could you could definitely imagine bifurcating that between trying to enrich you know, the model. I think other people do things like this already and this is just a more sophisticated version, but you can you can have a fully sort of quantitative strategy and then you can enrich it with what you think is qualitative kind of human-designed analysis and then you can have a quantitative strategy to incorporate the qualitative. I think many of these multi-strat strats like do these sorts of things. And what he's doing is kind of a just a slightly version of different flavor of that.

这完全说得通，说得通。我不是……你知道，成功的方式有很多，而模型只是增加了更多自由度。It makes makes perfect sense. I'm not There are so many ways, you know, to be successful and models have just added more dimensions of freedom.

发言人：那你自己的投资组合呢？再多说一点，你是如何觉得持有某个仓位很安心的。先不管它是哪家公司。Speaker: But what about your own portfolio? Say a word more about how you feel comfortable having a position. Forget the company that it is

发言人：好的。Speaker: Sure.

说话人：考虑到有这么多研究都表明，用这种方式构建投资组合简直疯狂，这件事竟然还影响这么大。Speaker: that's that big in the face of like so much research that would suggest that's a crazy way to build a portfolio.

说话人：我认为，其中很多研究更多是在考察证券价格等方面的价格变化。Speaker: A lot of that research I think is more looking at price changes in terms of like securities prices

说话者：嗯。Speaker: Mhm.

发言人：不那么专注于像一个你知道的公司所有者那样思考这件事。我的意思是，我们说的是 Carvana。要不是我觉得 Carvana 的基础设施、品牌以及其他一切都如此与众不同，以至于它很难出现特别糟糕的结果，我可能就不会这么放心地持有这么多它的股票。嗯，你知道，也许事情就是这么简单。像是事先反复思量，你知道，采用那种投资组合策略当然也行得通，但如果你当年发现了沃尔玛、家得宝、Fastenal 之类的公司，你肯定没法充分利用它们。嗯，这大概就是我对 Carvana 的看法。你当然可以去读读历史，那些取得巨大成功的人往往都把投资集中在少数标的上。Speaker: and less focused on like thinking about it the way someone who you know, owns a company might think about it. I mean, we're talking about Carvana. Were it not for my sense that like Carvana's infrastructure and brand and everything else is just so differentiated that there are few scenarios where it works out really badly. Um you know, then I wouldn't feel so comfortable owning so much of it. It might just be as simple as that. Like over thinking over you know, that portfolio approach would have worked absolutely, but it's certainly if you discovered Walmart or Home Depot or Fastenal or something back in the day, you wouldn't have taken full advantage of it. Um and that's kind of my sense, you know, with with Carvana. If you read history, of course, all the people who did really great tended to be very concentrated.

不过当然，历史上充斥着这样的例子，你不会去读相关的历史。是啊，你不会去读相关的历史，你没读过。我也觉得，那些文献中的很多内容，大致上是大多数追求平均或略高于平均回报的人应该做的，而且完全是正确的做法。嗯，但那不是我想要的。我很看好 Carvana。股价大幅上涨、让我觉得有必要分散投资，我也不会介意。只是这更像是在预期回报和明知自己承担了不同寻常的风险之间做权衡。But of course, the history is lit that you don't read the history of Yeah, you don't read the history of you didn't. I also think that a lot of that literature is sort of what most people who are looking for average or slightly better than average average results should do and it's totally the right thing to do. Um but that's not what I'm looking for. I like Carvana a lot. It wouldn't upset me any if the stock went up a bunch and I felt compelled to diversify. That would also be okay. It's just more of a trade-off um between expected returns and sort of knowingly taking a um an unusual risk.

教育与指导模型Education and directing models

你怎么看待教育？你我都有年龄差不多的孩子。聊聊你是怎么考虑他们的教育的吧，也谈谈这个国家那些推动教育发展的机构——它们在你所描述的那段具有路径依赖性的历程中，发挥了非常重要的作用。What do you think about education? You and I have kids of roughly similar ages. Comment a little bit on how you're thinking about their educations, the institutions that have driven education in this country that have been really important in that whole path-dependent story you've described.

你觉得教育的当下未来会是什么样子，或者应该是什么样子？我一直在跟孩子学校的校长聊这个问题，聊着聊着，感觉他的想法，嗯，还有我的想法，越来越朝着这个方向发展：在教育孩子时，你所追求的东西正变得越来越两极化。对吧？让我先退一步说。聪明有什么好处？What do you think the present future of education looks like or should look like? I've been like talking to the head of my kids' school about this and it more and more it seems to be that his thinking and you know, and my thinking are heading in this direction of like the way you're educating kids is getting more bimodal in terms of what you're looking for. Right. Let me step way back. What is the benefit of intelligence?

说话者：在一个模型拥有无限智能的世界里，个人智能，也就是你自己的智力。你可能会说：“哦，这没用。无所谓。一个 IQ 只有 50 的人，和一个 IQ 有 150 的人一样能在这个世界上应对自如。”我觉得这不太对，直觉上也不对。但我想，要弄清楚这一点，关键在于：你的智力越高、结晶智力越强（也就是受教育程度越高），就越擅长和模型沟通，告诉模型你想要什么。比如说，我要和模型深入讨论，进而设计前沿的量子物理实验，就会非常吃力，因为我对量子物理一无所知。模型可以很努力地给我解释，但到头来，它只能用蜡笔和 Nerf 玩具那种方式来解释。Speaker: Personal intelligence, like your own intelligence, in a world where there's infinite intelligence from models. And you might say, "Oh, it's useless. Like doesn't matter. Like someone with a 50 IQ can navigate the world just as well as someone with a 150 IQ." And I don't think that's quite right. And intuitively, it doesn't feel right. But I think just to nail down where it is that the more intelligence you have and more crystallized intelligence you have, which would be sort of education, the better you are at communicating with a model and telling a model what you want. So, like I would be have a very tough time like having a deep conversation with the model to like create leading like quantum physics experiments because I don't know anything about quantum physics. And the model could try very hard to explain things to me, but in the end it would be explaining things in crayon and Nerf.

而且，就像是，要达到前沿水平，并充分发挥模型的潜力，确实会非常困难。相反，在我有一定专业知识的领域里，我和模型就能比较有效地沟通，也能更好地引导模型按照我的意愿行事。所以，能够引导模型按你的意愿做事非常重要。我认为这将会是未来的一项重要技能。要做到这一点，你确实得对世界有所了解。你得有一定的知识，而聪明才智也会有所帮助，教育也有帮助。比如说，如果你不知道什么是随机过程，就没法要求它假设这是一个随机过程。当然，它确实可以教你什么是随机过程，但这样做会带来很多额外的阻碍，而不是直接把这些事情弄明白。And like it just like I we just like it would just be very hard to get to leading edge and like have a and get like the best out of what the model has to offer. Um whereas in an area where I do have some expertise, um the model and I can communicate kind of effectively and I can direct the model to my to my will like much better. And so, there's like a super important like you to direct a model you to do your will, which I think is going to be an important skill in the future, you do have to understand something um about the world. You have to be you know and smarts help for that. And education helps for that. Like you can't ask it to you know, assume this is a stochastic process unless you know what a stochastic process is. Now, you certainly it could teach you what one is, but that's a lot of friction instead of this figuring this all out.

你不一定非得自己算出数学题，但你得知道它是什么，对吧？所以，我一直在思考教育问题：有一种想法是，你要教给孩子一系列相互依托的基础技能。我认为，最终他们必须能够在没有模型辅助的情况下运用这些技能。这并不是因为他们将来会在没有模型辅助的情况下使用这些技能，而是因为如果他们不懂乘法，就很难理解矩阵乘法。如果他们对矩阵乘法的理解还没有达到足够高的层次——他们不必非常擅长实际运算，但必须能够理解模型在运算时做的是什么——就无法指导模型。这也是为了让他们能够引导模型。因此，我越来越认为，我们应该花大量时间让孩子暂时脱离这些模型，教他们一些我们认为重要的基本技能。至于这些技能究竟是什么，这个问题很难回答；但核心想法是：没错，模型可以做到，可你得自己懂，才能告诉模型该做什么。You don't have to work out the math, but you have to know what it is, right? And so, when I've been thinking about education, there's like some this idea that like you're teaching your kids a bunch of foundational skills that build on one another where I think in the end the sum total of those they have to be able to do them kind of without models. Not because they're going to do them without models in the future, but because if they don't know how to do multiplication, it's going to be really hard to understand matrix multiplication. And if they don't know what matrix multiplication is at a at a high enough level, they don't have to be able to do it really well, but they have to be able to understand what the model is doing when it's doing it. And that's so they can direct the model. And so, increasingly I'm of the view that like we should spend a bunch of time with kids basically separated from these models teaching them basic skills that we think are important and what those are is a really hard thing, but the idea being that yes, model can do it, but you need to know it so that you can tell the model what to do.

另一方面，有些英语老师会说：“我不喜欢这些模型，因为孩子们会用它们在莎士比亚论文上作弊。”我就会说：“你们的抱负也太小了吧。对吧？如果孩子们是在莎士比亚论文上作弊，”And then on the flip side, some English like I don't like models because like the just cheat on their Shakespeare essays. I'm like you're not being ambitious enough. Right? If the kids are cheating on their Shakespeare essay,

说话者：给他们安排更难的任务。Speaker: Give them something harder to do.

说话人：给他们安排难一百倍、也更有雄心的任务，让他们把模型的能力发挥到极致，对吧？让他们把成果拿回来，然后确保他们不是只给模型一个提示词，就让模型替自己做作业。要确保他们真正掌握这些成果。问他们为什么会这样，对吧？逼着他们为此作出解释，对吧？我觉得这也是另一件非常重要的事，因为孩子们需要擅长使用这些模型，而且坦白说，他们也需要被教会自己什么都能做。Speaker: Give them something 100 times harder and more ambitious and have them use models to their fullest extent. Right? Have them bring the work product back and then make sure that they didn't just give a prompt to the model do the homework. Make sure they own it. Ask them why it's like this, right? Force them to like justify it, right? And I think that's another really important thing cuz kids need to be good at using them and they also have frankly just need to be taught they can do like anything.

收尾对话与赞助商Closing conversation and sponsors

发言人：你我时不时会通一次电话，每次聊完我都希望能把通话录下来，让其他人也能听听，因为我能学到很多。我觉得今天这场对话尤其如此——它算是几个月前我们那次通话的一个变体，里面有很多值得思考的内容，关乎智能的未来，以及这对市场和公司意味着什么。呃，这种看法并不是和你听到的最主流观点相对立，而是提供了一个不同的视角。最主流的观点大概是：无限扩建，永远扩建，永远做空一切，嗯，对智能的需求没有上限。Speaker: You and I get to talk every so often on the phone every time I wish it was recorded so that other people could hear it cuz I learn so much and I think in this particular conversation today, which is sort of a version of a call we had several months ago, it's just such a valuable set of food for thought about the future of intelligence and what that means for markets and companies and uh is a little bit not in opposition to but a different perspective than the most common one that you're hearing, which is sort of unlimited, you know, unlimited build out forever, short everything forever, um no cap on the demand for intelligence

发言人：嗯，我很喜欢之前在电话里和今天跟你聊这件事，非常感谢你抽出时间。Speaker: and uh I've loved talking to you about it both on the phone and today, so thank you so much for your time.

说话人：谢谢。你知道微小的优势会随着时间不断累积吗？这在投资中如此，在经营公司时也同样如此。你的支出系统就是你的资本配置策略。Ramp 默认就能让它变得更智能。随着时间推移，数据更优、决策更好、经济效益更佳。访问 ramp.com/invest 了解详情。随着业务增长，Vanta 也会伴你一同扩展，自动化合规流程，并为安全与风险管理提供统一的信息来源。访问 vanta.com/invest 了解更多。从 OpenAI、Cursor 到 Perplexity，最优秀的 AI 和软件公司都在使用 WorkOS，让产品一夜之间而非数月后就做好服务企业客户的准备。访问 workos.com，跳过那些枯燥乏味的基础设施工作，专注打造你的产品。Ridgeline 正在重新定义资产管理技术，将自己定位为真正的合作伙伴，而不仅仅是软件供应商。Speaker: Thank you. You know how small advantages compound over time? That's true in investing and just as true in how you run your company. Your spending system is your capital allocation strategy. Ramp makes it smarter by default. Better data, better decisions, better economics over time. See how at ramp.com/invest. As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk. Learn more at vanta.com/invest. The best AI and software companies from OpenAI to Cursor to Perplexity use WorkOS to become enterprise ready overnight, not in months. Visit workos.com to skip the unglamorous infrastructure work and focus on your product. Ridgeline is redefining asset management technology as a true partner, not just a software vendor.

他们帮助企业将规模扩大至原来的5倍，实现更快增长、更智能的运营，并获得竞争优势。访问 ridgelineapps.com，看看他们能为您的企业释放哪些潜力。They've helped firms 5x in scale, enabling faster growth, smarter operations, and a competitive edge. Visit ridgelineapps.com to see what they can unlock for your firm.