The Economics of AI Usage and What's Next For SaaS | Benedict Evans on a16z

Agentic coding went from being kind of useful to really changing everything. Agentic 编程从某种程度上有用，变成了真正改变一切的东西。 It was going to be [music] magic. 那将会是魔法。 And in 20 years time, we'll just say, "Well, of course that's how it is. 二十年后，我们只会说：「当然就是这样， Computer's always done that." 电脑一直都能做到这些的。」 Every year, Silicon [music] Valley awaits Benedict Evans presentation. 每年，硅谷都在等待 Benedict Evans 的演讲。 Former A16Z partner, author of one [music] of the industry's most read newsletter, The Mind Behind AI eats the world. 前 a16z 合伙人，业内阅读量最高的 newsletter 之一的作者，也是《AI Eats the World》背后的核心人物。 We are in this extreme scarcity. 我们正处于极度稀缺之中。 Like, we can't spend [music] $10 trillion a year on AI infrastructure cuz there isn't$10 trillion a year there to spend on it. 就是，我们不可能每年在 AI 基础设施上花 10 万亿美元，因为根本就没有那么多钱。 The big part of your thesis is this idea that models are [music] going to end up as commodities. 你的核心论点之一，是模型最终会变成商品化的东西。 I don't think foundation models are a product. 我不认为基础模型是一个产品。 I don't think a chatbot is a product. 我不认为聊天机器人是一个产品。 I think the value will be further up. 我认为价值会在更上层。 Explain the reasoning that they're a bit in what they look like. 解释一下这个逻辑，以及大概会是什么样子。 There's like three or four building blocks you can put on the table. 桌上可以摆三四个构件。 One of them is 其中一个是 Benedict. Benedict。 Welcome back to the Acing Z podcast. 欢迎回到 a16z 播客。 Thank you. 谢谢。 Last time you were here, we were discussing the first iteration of your presentation AI use the world. 上次你来，我们聊的是你的第一版演讲《AI Eats the World》。 Uh you know, you since wrote it almost a, you know, year and a half ago. 那已经是大概一年半前的事了。 At at this point we're get you always begin your presentation with your 每次你开始演讲，都会列出那些大问题，但这次在进入「未来大问题」之前， you know what are the big questions but I'm curious this time first before getting into the what are questions going forward 你知道，每次你列出那些大问题，但这次我想先请你回顾一下，在进入未来问题之前， I want you to reflect on what have we learned since you originally uh made the presentation what's played out um and let's reflect back on 我想先请你回顾一下，自从你最初做了那个演讲之后，我们学到了什么，哪些预判成真了， what's changed in the last year so I think we have much more of a sense of diverging product strategy we have much more a s of a sense of kind of competitive tension that goes beyond just make a bigger model faster with more about more more compute. 这一年发生了哪些变化，我认为我们对产品策略的分化有了更多认识，对竞争张力也有了更清晰的感知，这种张力已经不只是「谁的模型更大更快用更多算力」了。 Um we've had several iterations of open AI strategy in particular from sort of everything all at once yesterday to oops no maybe we should double down on coding. OpenAI 的策略经历了好几轮迭代，从「昨天要什么都做」到「不对，应该把重心押在编程上」。 Um clearly agentic coding started working and so all the focus in tech has kind of narrowed in massively onto that as something that has absolute product market fit in the sense that like the customers are pulling it out of your hands. Agentic 编程开始真正跑通了，所以整个科技圈的焦点开始大幅收窄，聚到这件事上，因为它有真实的产品市场契合，就是客户直接从你手里抢着要。 Um and um and of course that comes with the supply crunch around capacity and price imbalance imbalance of supply demand capacity capex pricing that we see at the moment. 当然，这也带来了当前算力和产能的紧缺，以及供需、产能、CapEx 和定价之间的失衡。 Um so that's kind of the big shift like we had a moment of like this is kind of sort of working and kind of exciting but we're not quite sure what we're going to do with it to like right it works for coding um will it work for anything else like yes almost certainly but that's what's working right now and so that's become we've got this kind of much narrower focus. 这就是这波大转变，从「这东西有点用、还挺有意思，但不确定能用来做什么」变成了「好，编程上跑通了，其他地方能不能跑通？几乎肯定能，但编程是当下唯一跑通的」，然后我们的视野就大幅收窄了。 Um otherwise um you know the chartman numbers keep coming up, the models keep getting bigger, the capex keeps growing, the usage keeps growing, people using this more. 其他的，用量数字一直在涨，模型越来越大，CapEx 一直在涨，用量在涨，人们越来越多地在用。 But most of the sort of fundamental questions you might have had two or three years ago didn't really have answers. 但两三年前你可能有的那些根本性问题，大多数还是没有答案。 Like we don't know if there'll be a winner in the models. 模型里会不会出现赢家，我们不知道。 We don't know if they can capture value up the stack. 它们能不能在堆栈上层捕获价值，我们不知道。 We don't know how much the models can do. 模型能做到什么程度，我们不知道。 Um we don't see a way that consumers will use this daily rather than weekly with the technology we have right now. 以现有技术，消费者每天使用而不只是每周使用，我们还看不到实现路径。 So all all of those questions are still open. 所以这些问题还是全部开放着。 Yeah. 对。 And just on on the on the coding, how could could we have figured 关于编程这件事，我们本来有没有可能预见到， could we have foreseen that that would have been the the the use case that really would have taken off or what's sort of a reflection on that? 能否预见到那会是真正爆发的用例，或者你对这件事有什么回顾？ Well, um you deterministically you could have said, well look who's messing about with this stuff? 从决定论的角度，你可以说，好，那谁在摆弄这些东西？ Software developers. 软件开发者。 What are software developers going to try and make work software development? 软件开发者会去让什么东西跑起来？软件开发。 Um so you know at a very kind of simplistic naive level 所以，从非常简单朴素的层面来说， well yeah the stuff that should work is software develop first is software development just as like kind of I often compare this moment to like the internet in like 9798 but it's also like the PCs in the early 80s or the late '7s. 是的，应该最先跑通的是软件开发，就好像我常把这个时刻比作 1997、98 年的互联网，也像 80 年代初或 70 年代末的 PC。 It's incredibly exciting but it's not quite clear what it's for and it doesn't quite work yet and clearly the first thing that people did with PCs was make computers. 极度令人兴奋，但不太清楚是用来干什么的，也还跑不太通，而且当年人们用 PC 做的第一件事就是造计算机。 Um, and the first thing that people are doing with LLMs, in a sense, LLMs are computers, is to make more compute. 用 LLM 做的第一件事，某种程度上 LLM 就是计算机，是制造更多算力。 Um, and so that's not terribly surprising. 所以这其实并不奇怪。 I think the shift is been at the beginning of this year clearly that agentic coding went from being kind of useful to really changing everything. 我认为这一年初发生的转变是：Agentic 编程从某种程度上有用，变成了真正改变一切。 And I'm not sure you could have you clearly there were people who were going to say, well, this is going to be able to do absolutely anything. 我不确定你是否能预见到，当然有人会说，这东西什么都能做， And so they will say, well, yes, look, I told you. 所以他们会说，好，你看，我说了吧。 Um, but I don't think anyone kind of kind of could have deterministically predicted exactly when that was going to happen and that it was going to be coding it would work first. 但我不认为任何人能准确预测，这件事是什么时候发生的，以及会最先在编程上跑通。 And and what have we learned about sort of uh, you know, say more about what this means for engineers, junior engineers, senior engineers, sort of the the jobs discussion, how teams are organized, uh, etc. 那我们对工程师、初级工程师、高级工程师的影响，团队组织方式等，到目前为止我们了解到了什么？ What have we learned so far? 我们目前了解到了什么？ I don't think we've learned anything. 我觉得我们什么都还没学到。 I mean, you know, this this didn't this didn't this didn't work six months ago. 我的意思是，这六个月前还跑不通。 Yeah. 对。 and everyone is scrambling around trying to work out what it means. 所有人都在慌忙摸索这意味着什么。 And you know, you can get very very into the noise and the detail and what did somebody say at a party yesterday. 可以非常非常深入到噪音和细节里，比如昨天的派对上谁说了什么。 So, oh my god, that's how it's all going to work. 天哪，原来这就是一切的运作方式。 Um, you know, it's going to take a couple of years for this all to settle down. 这些事情沉淀下来需要几年时间。 You know, if nothing else because of the pricing, you know, we've got this enormous crunch between the demand and the supply and hence the pricing. 光是定价这一块，就有这么大的供需压力，所以也影响着定价。 Um, so we don't know what, you know, what a team is going to look like. 所以我们还不知道团队会是什么样子。 I think people are asking new questions around, you know, the sort of the obvious one of, you know, do you hire junior people and if so, what are they doing and why were you hiring junior people in the past and were you actually hiring to do the thing that they did or were you hiring them to do something else? 人们开始问新的问题，比如最显而易见的：你还招不招初级员工，如果招，他们在做什么，你过去为什么要招初级员工，你到底是请他们做那件具体的事，还是让他们做别的什么？ And so if you automate away a class of stuff that used to get done by people, then what will happen? 如果你把一类原来由人完成的工作自动化了，那会发生什么？ And that's sort of becomes much more real now in software development because you actually are automating a bunch of stuff that used to be done by people. 这个问题在软件开发领域变得非常现实，因为你确实在自动化原来由人完成的一大堆工作。 So those questions are kind of now rather than theoretical. 所以这些问题现在是真实的，不再只是理论上的。 But I don't think anybody can possibly say they kind of know what the market structure is going to look like or what the career of a software engineer is going to be in three years time. 但我不认为任何人能说，他们知道市场结构会是什么样，或者软件工程师三年后的职业路径会是什么。 I think it would be you'd be insane to think that you could know that yet. 我觉得你要是现在就声称知道这些，那才叫奇怪。 Yeah. 对。 the talk about uh open AI uh talk about what's most uh surprised you or how have you kind of made sense of their sort of strategy development and and the questions that they have going forward. 聊聊 OpenAI，最让你意外的是什么，或者你怎么理解他们的策略演变，以及他们接下来面临的问题。 Well, you know, it's always been such a such a a tranquil drama-free environment. 是啊，这一直是个如此平静、没有任何戏剧性的环境。 So, you know, it's [laughter] and you know, obviously they've had the issue with with with Fiji Simo having to take a medical leave um which kind of shuffled things up a bit. 所以，就是，显然他们遇到了 Fidji Simo 因病请假的问题，把事情稍微搅动了一下。 Look, clearly the second half, last quarter of last year, this their question was right, well, the models are the models, but what else? 很明显，去年下半年最后一个季度，他们的问题是：模型就是模型，但还有什么？ And how do we get people to do other stuff with this? 怎么让人们用这个东西做别的事情？ You know, ask chat GP GPT for 15 ideas for what we could do to build value on top of infrastructure, and then we'll do all of them. 让 ChatGPT 给出 15 个基于基础设施构建价值的想法，然后全部去做。 It's almost literally what what it looked like. 几乎字面上就是这个意思。 And then um um Anthropic with having less capital raised said, "No, we're going to focus on coding." 然后 Anthropic 融资少一些，说：不，我们要专注在编程上。 And they got coding working. 然后他们把编程跑通了。 Um whether that was like a deliberate strategy or kind of they stumbled into it is you know for other people to say but like clearly that worked but the question kind of still remains. 这是蓄意为之还是撞上的，只有当事人知道，但这件事显然成了，问题还是悬在那里。 It's like the stuff that's working right now is software development and some things in some other fields and then there's a lot of people who are kind of excited about using this around the edges and using this for some things and there's clearly this kind of very widespread between people in the valley who bought you know a cluster of Mac studios and are running Claude Code all day versus um you know those other 40% of people who say yeah it's kind of useful um I used it last week for something [laughter and gasps] and I'm like how do you br how do you bridge that and I don't think that question you soft software is a place where that's really really bridge jumped over that bridge and I don't think and then there's a lot of other places where people are kind of scratching their heads and using it up to a point and then there's a lot of places where corporations are using it to automate some like specific back office process where you're not asking the user to work out what they do with the new tool instead you're saying okay here's a problem that we can solve and you know I go and talk to, you know, companies outside America and outside of tech and talk to consultants and um, you know, investors. 就是，现在跑通的是软件开发，以及一些其他领域的少数用例，还有很多人在边缘地带用这个，用于某些事情。硅谷里那些买了一堆 Mac Studio 集群、整天跑 Claude Code 的人，和那另外 40% 说「还可以，上周用了一下」的人之间，差距巨大，我不知道怎么弥合这个鸿沟。软件开发领域是真的跨过去了，其他很多地方的人还在挠头，用到一定程度就停了。还有很多企业在用 AI 自动化某些特定的后台流程，你不是要用户自己去想怎么用新工具，而是说，好，这是一个我们能解决的问题。我在美国和科技圈以外的公司、咨询顾问、投资者那里听到的， They're looking at those one at a time point solutions. 都是在一个一个地看这些点式解决方案。 Um, so like I'm speak couple of days ago to a commodities company and they want to use LLMs to get better predictions on their cash flow because they deal with all sorts of small producers and they don't necessarily know when their invoices are going to get paid and it's a very low low margin business. 就是，前几天我和一家大宗商品公司聊，他们想用 LLM 来更好地预测现金流，因为他们的供应商都是小生产商，不确定什么时候能收到款，而且这个行业本来利润率就极低。 So that's a big deal and so they want to use LLMs to get better cash flow forecasting. 所以能提高现金流预测精度对他们来说意义很大，他们想用 LLM 来做这件事。 That's very different thing from kind of going to catch EBT or Claude and saying hey you know [snorts] give me a summary of my meetings this week. 这跟去找 ChatGPT 或 Claude 说「给我总结一下本周会议」是完全不同的两件事。 Yeah. 对。 H 嗯 can you share how uh how did this compare with mobile um or other sort of platform in terms of you know 这和移动互联网或其他平台转移相比，在用户早期采用上是什么感觉？ use user early user adoption on on sort of the you know weekly or daily user. 比如每周活跃用户还是日活用户这方面。 So I think there there's there's there's a bunch of different ways to answer this. 我觉得有好几种不同的方式来回答这个问题。 One of them is like we're always standing on the shoulders of giants and the growth is always compounding. 一种是，我们总是站在巨人的肩膀上，增长一直在复利叠加。 So mobile didn't need to wait for um the internet or cellular networks like mobile data. 移动互联网不需要等互联网出现，不需要等移动数据，也就是蜂窝网络数据。 Mobile internet didn't need to wait for it kind of needed to wait for cellular data but it didn't need to wait for like the internet to happen and the internet didn't need to wait for PCs and PCs didn't need to wait for consumer electronics and semiconductors and so on. 移动互联网不需要等到互联网出现，某种程度上需要等蜂窝数据，但不需要等互联网，互联网不需要等 PC，PC 不需要等消费电子和半导体，以此类推。 So you've always got this accelerating adoption and you know when when when your boss my old boss Mark Andre was working on Netscape there were like doubledigit millions of PCs on the entire planet. 所以你总能看到这种加速的采用，当你的老板，我的老老板 Marc Andreessen 在做 Netscape 的时候，全球 PC 总数不过是两位数的百万量级。 So like no you couldn't have 900 million weekly active users because there weren't 900 million PCs. 所以不可能有 9 亿周活用户，因为全球根本没有 9 亿台 PC。 So there's always that acceleration. 所以总是有这种加速的过程。 So that's one point. 这是第一点。 I think the second point is like at the early stage of any of these shifts it's not really clear how it's going to work and nothing works. 第二点，在任何这类转变的早期阶段，都不太清楚会怎么演进，而且什么都跑不通。 So you know like I'm just about old enough to remember this. 我勉强还老到记得这些。 I'm not sure how how how old you are, but like, you know, anyone in their 30s doesn't really remember a time when it was completely normal that you'd be working and then everything on the screen would just freeze and you just have to crawl under your desk and unplug the computer and then pray that like some of what you done in the last hour might still be there. 不知道你多大，但 30 多岁的人大概不太记得，那时候完全正常的事是：你在工作，然后屏幕上的一切突然冻住了，你只能爬到桌子底下拔插头，然后祈祷过去一小时做的东西还有一些留下来。 That just doesn't happen anymore. 这种事现在完全不会发生了。 [snorts] [snorts]