The State of AI: Models, Moats, and the Consumer Renaissance
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投资者 Anish Acharya 对当前人工智能格局进行了全面分析，强调其令人难以置信的丰富性和未来潜力。他以一个个人轶事开场：他使用“GrokBot”根据一张照片和一个价格限制，自主研究并购买了牛仔裤，这突显了人工智能日益增长的机智和产品化能力。
**宏观市场与 AI 的发展轨迹**
Acharya 认为“泡沫”论调已被过度讨论，反而提出我们可能“不够乐观”。他指出，GPU 价格在无限需求和有限供应下不断上涨，这表明其潜在增长强劲。他相信，SaaS 市场尽管近期波动剧烈，但仍保持弹性，软件支出是企业预算中至关重要但并非过大的部分。大多数传统商业护城河（如网络效应、规模、品牌）一如既往地强大，不受 AI 影响，而“整合护城河”（如 SAP 的复杂性）则最容易受到编码代理的攻击。
**应用层：智能的产品化**
Acharya 将智能概念化为一种“原语”，就像云计算一样。那么，应用层将这种原语产品化，以提供特定的经济成果。他反对人工智能领域“两强争霸”的说法，列举了 XAI 等新玩家的迅速崛起，以及 Anthropic 和 OpenAI 等公司的持续增长。他指出，每个模型都具有比较优势和领域级专业化。例如，OpenAI 的新 GPT 模型在知识工作方面表现出色，而 Claude 则高度倾向于软件工程。这种专业化意味着实验室正在垂直整合 *向下* 到推理和计算层，而不是 *向上* 到应用层，因为应用层过于特殊且运营成本高昂。
此外，模型表现出不同的“人格特质”，例如神经质（字面、精确）与开放性（创造性、大胆）。企业需要这两种特质，这促成了多模型方法。这也催生了“模型聚合”，即应用程序结合多个模型——类似于 Expedia 聚合航空公司——以在编码（例如，使用前沿模型进行规划，使用较弱模型进行执行）、创意工具或研究等任务中提供超越部分之和的卓越结果。
**AI 使用的演变：从提示到循环**
人工智能的使用正在从简单的提示演变为复杂的“循环模型”（代理）。他用编码代理来阐释这一点，这些代理能够从报告到部署自主修复错误。这一概念延伸到其他业务功能，如价格优化和采购。最具雄心的“业务循环”甚至可以提出高层战略性变革，例如开设新分支机构。这种由 AI 驱动的“企业自动化”正在改变企业的运营方式，将编码智能视为堆栈中各种产品的原语，以满足不同用户和需求。
**消费级 AI：一个复兴时刻**
Acharya 宣布，现在是 AI 的“消费者季度”。历史上，消费者 AI 面临障碍：消费者不喜欢为软件付费，AI 软件的边际成本高，并且没有原生的 AI 分发渠道（如应用商店）。然而，开放权重模型正在大幅降低成本，更好的产品/设计正在使 AI 能力变得易于获取。他强调了两个关键领域：
1. **面向企业家的编码代理**：AI 使“数字原生企业家”（不一定是程序员）能够构建创收的软件产品（例如，“夫妻店 SaaS”），超越了传统的创作者角色。
2. **个人代理**：GrokBot 和 Town 等产品正在使个人代理（管理任务、自动化电子邮件、优化订阅）对普通消费者而言变得触手可及。这些代理通过随着时间推移学习用户上下文来建立“复利价值”，类似于一位“资深员工”。
Acharya 相信，消费级 AI 将带来“生活质量的显著改善”，AI 将管理围绕家庭、友谊、金钱和健康的“循环”。他指出，虽然科技通常能提升智力，但 AI 现在通过情感和人际交往领域触及“人性”，开辟了巨大的新产品类别，而大型科技公司可能因文化限制而无法涉足。
**经济学与创始人**
他承认毛利率上的细微争论，但指出人们对 AI 应用有着“非同寻常”的支付意愿，甚至高达每月 200 至 2000 美元，这预示着“奢侈软件”的崛起。在创始人方面，他看到了一种新的原型：更少的 MBA，更多的研究人员，通常处于职业生涯的早期。尽管他们可能在商业成熟度上较低，但他们技术成熟度极高，并且缺乏对“可能之事”的“先入之见”，这使他们能够构建非凡的事物，挑战“想法过于宏大”的旧有观念。这种能力的丰富意味着今天的风险常常是“想法过于渺小”。
对于中小企业，传统营销渠道依然存在，但一个关键领域是“新业务的形成”（例如，一个 25 岁的年轻人为当地社区构建 SaaS），这通常依赖于强大的口碑传播进行分发。Acharya 以乐观的展望结束，赞扬了这场“消费级建设者的复兴”。
Anish Acharya, an investor, provided a comprehensive breakdown of the current AI landscape, emphasizing its incredible abundance and future potential. He kicked off the discussion with a personal anecdote about using "GrokBot" to autonomously research and purchase jeans based on a photo and a price limit, highlighting AI's growing resourcefulness and productization.
**Macro Market & AI's Trajectory**
Acharya dismisses the "bubble" narrative as over-discussed, suggesting instead that we might be "insufficiently optimistic." He points to indicators like rising GPU prices amidst infinite demand and constrained supply, signifying robust underlying growth. He believes the SaaS market, despite recent whipsaws, remains resilient, with software spend being a critical but not disproportionately large part of enterprise budgets. Most traditional business moats (network effects, scale, brand) remain as strong as ever and are unaffected by AI, while the "integration moat" (like SAP's complexity) is most vulnerable to coding agents.
**Application Layer: Productizing Intelligence**
Acharya conceptualizes intelligence as a "primitive," much like cloud computing. The application layer, then, productizes this primitive to deliver specific economic outcomes. He argues against the notion of a "two-horse race" in AI, citing the rapid emergence of players like XAI and the sustained growth of others like Anthropic and OpenAI. Each model, he notes, possesses comparative advantages and domain-level specializations. For instance, OpenAI's new GPT models excel at knowledge work, while Claude is highly oriented towards software engineering. This specialization means labs are vertically integrating *down* into inference and compute, not *up* into the application layer, which is too idiosyncratic and OpEx-heavy.
Moreover, models exhibit different "personality traits," such as neuroticism (literal, precise) versus openness (creative, presumptuous). Businesses need both, leading to a multi-model approach. This also gives rise to "model aggregation," where applications combine multiple models—similar to how Expedia aggregates airlines—to deliver a superior "greater than sum of parts" outcome for tasks like coding (e.g., using a frontier model for planning, a lesser one for execution), creative tools, or research.
**The Evolution of AI Use: From Prompts to Loops**
The use of AI is evolving from simple prompting to complex "models in loops" (agents). He illustrates this with coding agents that can autonomously fix bugs from reporting to deployment. This concept extends to other business functions like price optimization and procurement. The most ambitious, "business loops," can even propose high-level strategic changes, like opening a new branch. This "enterprise automation" driven by AI is transforming how businesses operate, treating coding intelligence as a primitive for various products across the stack, catering to different users and needs.
**Consumer AI: A Renaissance Moment**
Acharya declares this "consumer's quarter" for AI. Historically, consumer AI faced hurdles: consumers dislike paying for software, AI software has high marginal costs, and there was no native AI distribution channel (like an app store). However, open-weight models are dramatically reducing costs, and better product/design is making AI capabilities accessible. He highlights two key areas:
1. **Coding Agents for Entrepreneurs**: AI enables "digitally native entrepreneurs" (not necessarily programmers) to build revenue-generating software products (e.g., "mom-and-pop SaaS"), moving beyond traditional creator roles.
2. **Personal Agents**: Products like GrokBot and Town are making personal agents (which manage tasks, automate emails, optimize subscriptions) accessible to everyday consumers. These agents build "compounding value" by learning user context over time, akin to a "tenured employee."
Acharya believes consumer AI will lead to a "dramatic quality of life improvement," with AI managing "loops" around family, friendships, money, and health. He notes that while tech often boosts intellect, AI now speaks to "humanity" through emotional and interpersonal domains, opening up vast new product categories that large tech companies might be culturally constrained from pursuing.
**Economics and Founders**
He acknowledges the nuanced debate on gross margins but points to an "extraordinary" willingness to pay for AI apps, even up to $200-$2,000/month, suggesting a rise of "luxury software." In terms of founders, he sees a new archetype: less MBAs, more researchers, often earlier in their careers. While they may have lower business sophistication, their dramatically higher technical sophistication and lack of "preconceived notions" about what's possible allow them to build extraordinary things, challenging the old wisdom that "ideas were too big." This abundance of capability means that today's risk is often that "ideas are too small."
For SMEs, traditional marketing channels persist, but a key segment is "new business formation" (e.g., a 25-year-old building SaaS for their local community), often relying on strong word-of-mouth for distribution. Acharya concluded with an optimistic outlook, celebrating this "renaissance for consumer builders."
摘要
Anish Acharya joins Jen Kha to break down the next frontier of AI, from the evolving model landscape and open-source AI to why the application layer, and consumer AI in particular, may be entering a new phase.
Anish explains why he believes there will be multiple winners at the model layer, why traditional moats like network effects, scale, and brand still matter, and how companies can choose between frontier and open-weight models depending on the economics of the task. They also explore why models are increasingly specializing, and how applications can combine different types of intelligence to create products that are more valuable than any single model.
The conversation then turns to consumer AI: personal agents that can shop and manage your inbox, coding tools enabling a new generation of small businesses, and why Anish thinks we're seeing a renaissance for consumer builders. They also discuss the changing economics of AI software, the rise of "luxury software," and why the biggest risk for today's founders may no longer be thinking too big, but thinking too small.
Timestamps:
00:00 - Intro
01:20 - Who Wins the AI Model Race in Three Years?
02:49 - What's Next in the Frontier of Intelligence
07:51 - Why Open Source Is the Only Option for Some Startups
19:57 - Redefining Consumer: When the Plumber Uses GrokBot
22:16 - Town Demo: Personal Agents & Managing Chaos
24:31 - One Dominant Personal Agent or Many Talking to Each Other?
25:26 - Apps vs Model Companies: Who Captures the Value?
29:16 - The New Economics of AI Apps: Margins, Compute & Capital as Moat
30:44 - Who's Actually Building Apps Today? Founder Archetypes
32:20 - Why Giving Founders Too Much Money Isn't Fatal Anymore
34:05 - Go-to-Market for Startups Selling to SMEs
Resources:
Follow Anish Acharya on X: https://x.com/illscience
Follow Jen Kha on X: https://x.com/jkhamehl
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GPT-4正在为你翻译摘要中......
中英文字稿 
为了帮我理清这一切丰富的信息，我将邀请 Anish Acharya 上台。 - 早上好，谢谢。你好。- 太好了，太好了，太好了。嘿，Anish。本周早些时候，Anish 和我在 GP 企业外的活动中碰面，他告诉我他已经在使用 GraphBot，并为自己买了好几条牛仔裤。Anish，你想分享一下你买了什么吗？没错，真实的故事。我将揭示一个重要的机密受保护知识产权，那就是我大多穿 Frame 品牌的牛仔裤。Frame 是个很棒的品牌。- 哇哦。- GrokBots 是个非常优秀的产品。我认为 GrokBots 的一个显著特征是它的资源运用能力。几天前我睡觉前说，“给我买一条这些风格的牛仔裤。” 我拍了我现有牛仔裤的照片，并嘱咐道，“不要花超过 500 美元，处理好就行。” 我早上醒来时，它已经调查了，找到了同款不同洗水的牛仔裤，用我的信用卡买下了，并且货物已经在运送中。
▶ 英文原文 ⏱
to help me break down all things around this incredible abundance. So, I'm going to bring up Anish Acharya. - Good morning, thank you. Hi. - Awesome. Awesome, awesome. Hey, Anish. Anish and I were at the GP offsite earlier this week, and he shared with me that he's already running GraphBot, and has purchased a bunch of jeans for him. So, Anish, do you want to drop what you purchased? True story, true story, yes. I'm going to reveal an important secret protected IP, which is that I mostly wear Frame jeans. Frame is a great brand. - Wow. - And GrokBots is an awesome product, actually. I'd say the kind of defining characteristic of GrokBots is sort of resourcefulness. You know, I went to bed a few nights ago and said, "Hey, buy me a pair of jeans that are inspired by these." I took a photo of my current jeans. I said, "Don't spend more than $500 and get it done." I woke up in the morning, and it had researched, found a pair, same fit, different wash, used my credit card, purchased them, and they're on the way.
所以我认为这种趋势会越来越明显。我们已经具备了相应的能力，现在的关键在于如何灵活应对，以及如何设计出大多数消费者都能理解的产品结构。太棒了，真的很棒。我告诉我的团队，我打算设定我的机器人，终于去处理那些我答应我丈夫要卖掉的堆积了两年的东西。这是我这个周末的计划。那么，安尼什，我们之前问过一个问题：三年后，今天的哪个人工智能领导者会成为明显的赢家？你怎么看？我认为会有多个赢家，而且我看到很多人也同意这种看法。比如说，在过去两周中，XAI从一个不太被看好的模型，迅速成为了三大竞争者之一。
▶ 英文原文 ⏱
So I think that is going to be something that we see more and more of. We already have the capabilities, and now a lot of the kind of unlock will come from resourcefulness and also the kind of product architecture delivered in a way that most consumers can understand. Awesome. Awesome, awesome. Yeah, I told my team that I'm going to set my bot to finally take care of the pile of things I've been promising my husband that I'm going to sell for the last two years. That is the project for this weekend. So, Anish, we asked the question earlier, which one of today's AI leaders will be the clear winner in three years from now? What's your take? I'm a many-winners guy, and I see I'm in good company with many of you. I mean, if you look at what's happened in the last two weeks, you know, XAI went from not even being a real contender on the model side to being, you know, one of three.
我们从一场双雄竞争意外地转换成了三雄竞争。你知道，在这一年中，我们注意到Anthropic从原本觉得自己非常有优势的状态，转变到OpenAI在最近三个月表现出色的局面。新的模型非常卓越，新的Codex Harness和ChatGPT桌面应用程序也做得很好。我们看到这些实验室在不同方向上的专业化正在加速，尽管彼此都在继续取得成功，但它们都在疯狂增长，同时XAI的OpenWeight也不例外。因此，我绝对支持多赢家的观点。有趣的是，在社交平台X上的情绪也是如此。虽然X并不总是未来的完美风向标，但往往至少可以早期反映出开发者的情绪。
▶ 英文原文 ⏱
So we extraordinarily went from a two-horse race to a three-horse race and, you know, even more broadly over the course of the year, we went from Anthropic feeling like they were so dominant they could do no wrong to OpenAI, who's just had an excellent three months. You know, the new models are exceptional. The new Codex Harness and ChatGPT desktop app is very well done. And we're seeing the sort of specialization in different directions of these labs. They're both growing like crazy, you know, despite each other's continued successes, XAI and his OpenWeight as well. So I'm definitely in the many-winners camp. Yeah, it's interesting to see the sentiment also on X, which is not always a perfect, you know, weather vane for the future, but oftentimes an early indicator of at least where developer sentiment is.
看来最近Claude在使用token等问题上受到了不少的反对和反馈。因此，开发者往往是"晴天粉丝"，他们会追随最新、最好的模型。我认为在过去的六到八周，我们将看到活动流动方面的一些非常有趣的进展。不过，Anthropics公司将在今年晚些时候上市，这也引起了很多关注。所以，这就直接引入了我们今天讨论的话题：在智能发展的下一个前沿，未来会是什么？太棒了，谢谢你，Jen。
▶ 英文原文 ⏱
And there's been a lot of pushback from Claude, it seems like, recently on people in terms of token usage, et cetera. And so, you know, developers tend to be fair weather fans on these things. They will go where the latest and greatest and very best model isn't. And particularly the last six to eight weeks, I think we're going to see some very interesting traction in terms of the flow of activity. But obviously, Anthropics going public later this year. And also, you know, there's a lot of keen interest on this. So with that, that actually brings us straight into the topic of discussion today. So where and what is next in the next frontier of intelligence? Amazing. Thank you, Jen.
让我为大家介绍一下。请随时提问。我们先来讨论宏观层面以及市场上的动态。然后，我们会广泛探讨应用层面，说明为什么应用是智能基础的产品化。最后，我们再谈谈消费领域。最近几周随着GrokBots和其他一些产品的发布，消费领域变得非常有趣。希望我们亲爱的朋友Leopold不介意我在这里开个玩笑。好的，请继续。
▶ 英文原文 ⏱
So let me tee this up for everybody. And please hop in if you've got questions. So let's first cover the kind of macro and what's happening at a market level. Then we're going to hop into the application layer broadly and sort of talk through why applications are the productization of the intelligence primitive. And then finally, let's talk about consumer. You know, with the launch of GrokBots and a few other products, it's actually been a very fun couple of weeks in consumer. Okay. Hopefully our dear friend, Leopold, doesn't mind me poking a little fun at him here with situational awareness, please. Next.
好的。我认为关于这是否是一个泡沫的讨论已经过度进行，或者至少已经完全讨论过了。实际上，有一个不太被讨论的话题是：如果我们低估了乐观程度会怎样？如果你观察一些潜在指标，它们实际上指向无限的需求和高度受限的供应，比如B 200这种不算尖端的GPU，价格却在按小时上涨。这很奇怪。通常我们看到这些东西是高度通缩的，这反映出供应非常紧张，而需求几乎是无限的。
▶ 英文原文 ⏱
All right. Look, I think that the kind of case for this being a bubble is over sort of discussed or at least fully discussed. I think actually the out of distribution topic that's less discussed is what if we're insufficiently optimistic? And if you look at some of the underlying indicators, what they point to is essentially infinite demand and highly constrained supply, you know, things like B 200, which is a non sort of cutting edge GPU prices going up on a per hour basis. That is very strange. Normally we see these things be highly deflationary and it sort of points to very constricted supply and essentially infinite demand.
所以我们一直在思考和讨论，在考虑到这些第二层指标的情况下，乐观的合理依据是什么。SaaS泡沫，或者说SaaS市场的剧烈波动，是一个观察市场心理有趣的窗口。你看，在二月份的时候，我们看到大量SaaS公司股票下跌了30%到40%，我们就说市场对软件的抛售过度了。没想到，现在很多公司的股价又涨回了40%。我不太确定我们在这过程中到底取得了什么成果，但我可以告诉你，我们那时说的话，现在依然适用，那就是对于企业来说，软件支出只占8%到12%，比例并不算高。
▶ 英文原文 ⏱
So we're thinking and talking a lot about what's the kind of informed case for optimism here, given some of these second order indicators. The SaaS bubble was a very, or the SaaS sort of, you know, whipsaw was an interesting peek into market psychology. You know, back in February when we saw this, you know, 30 to 40% drawdown on a bunch of SaaS names, we said that the market is oversold software. Lo and behold, here we are. Many of those names are back up 40%. And so I'm not quite sure what we collectively accomplished, but I'll tell you what we said then, which is still true today, which is for the enterprise, software spend is eight to 12%. It's just not a huge proportion of spend.
要自己编写工资或客户关系管理（CRM）系统的优势并不是特别大，而潜在的劣势则是无限的。不难想象，如果像工资这样的事情处理不当，可能引发各种合规性问题。因此，如今大多数企业软件都需要一种编程代理无法提供的精确度。然而，有一点变化是显而易见的，社会对这些软件的热潮正在退去。许多SaaS公司过去经历了大量的基于股票的薪酬（SBC）以及其他扭曲其经济表现的因素。现在，这些问题已经变得十分明显，这些公司必须加速发展，否则就可能面临淘汰。
▶ 英文原文 ⏱
So the upside to vibe code your own payroll or CRM is not particularly high. The downside is essentially unlimited. You know, obviously there's all kinds of sort of compliance implications of not getting things like payroll right? So most enterprise software today demands a level of precision that just isn't afforded by coding agents. The one thing that has happened, though, is the sort of tide has receded. So for a lot of SaaS companies had a ton of SBC and, you know, things that distorted their economic performance. I think that's very much visible now and they're going to have to sort of accelerate or die.
所以，让我们针对SaaS市场进行讨论。接下来，或许我们在那儿一起思考了几个月，但仍然有一些本质上的问题需要解决。你知道的，关于护城河一直有很大的讨论。是否真的有护城河？护城河已经不存在了。而这很有趣，因为如果你真正研究护城河——我认为在《七种力量》这本书中对护城河进行了著名的定义，这是我最喜欢的书之一——你会发现，大多数护城河实际上不受大量低成本信息的影响。当你想到网络效应和出现在分销中的规模效应、我们在硅谷往往会忽视的品牌效应时，这些因素一直都和过去一样有效。
▶ 英文原文 ⏱
So let's leak for the SaaS sort of market. Then perhaps we all collectively thought for a few months there, but still some sort of existential questions to address. You know, there's been a huge sort of discussion of moats. Are there any moats? There's no more moats. And it's very funny because if you actually study moats, which I think are most famously codified in the book Seven Powers, that's one of my favorites, the vast majority of moats actually are not affected by abundant, low cost intelligence. You know, when you think about network effects, scale effects, which shows up in distribution, brand effects, which we tend to discount in Silicon Valley, these things are as good as they've ever been.
你知道吗，再多的编码代理也无法改变耐克的本质，它仍然是耐克。而Instagram的强大从来不是因为Instagram应用程序的构建有多复杂，而是在于它背后的社交网络。因此，我实际上认为大多数竞争壁垒仍然和以前一样坚固，并且对构建持续增长的价值仍然至关重要。不过有一些壁垒可能会受到挑战。在我看来，集成壁垒是最明显的一个。比如，SAP的集成是出了名的复杂，以至于从一个SAP版本迁移到下一个版本都是一种生存风险。
▶ 英文原文 ⏱
You know, no amount of coding agents is going to make Nike, not Nike. And Instagram, the power of Instagram was never the complexity of building the Instagram app. Of course, it was the kind of network behind it. So I actually think the majority of moats are as good as they've ever been. And of course, are still critical to building compounding value. There are a couple of moats that are exposed. For me, the integration moat is the most obvious one. You know, SAP is so famously complex to integrate into and out of that it's a sort of existential risk to even migrate from one version of SAP to the next.
编程代理的出现让这一切变得大不相同。我认为，对于系统集成商（SIs）和全球系统集成商（GSIs）来说，有一个有些存在主义的问题，即当他们历来是作为一种集成枢纽时，他们的价值将会是什么。所以我认为他们原有的护城河有些面临风险。但是，其他传统的护城河仍然存在，并且仍像过去一样重要。我觉得这是一个非常重要的概念。当你开始思考哪些企业职能能创造超额收益时，通常是产品、销售、工程和研究这些领域。
▶ 英文原文 ⏱
Coding agents makes this dramatically better. I think there's a bit of an existential question, actually, for SIs and GSIs as to what will their value be when they've historically been this sort of point of integration. So I do think this moat is a little bit at risk. But for the other traditional moats, they persist and they're as important as they've ever been. And I think this is a really important concept. You know, as you start to think about what are the job functions in the enterprise that are alpha creating, it's typically product, sales, engineering, research.
相反，企业中有哪些工作职能是支持其他部门运作的，虽然称之为“行政”可能显得过于简单化，但它们确实承担了支持功能，比如法律、HR、财务等。我们认为，合理的架构以及正在形成的趋势是，对于有无限增长潜力的工作，比如销售或产品开发，你总是希望使用前沿的资源（例如，前沿的代币）。原因在于，你无法确切知道新产品功能的价值或签下一个客户账户的价值，因为这些价值实际上是没有上限的。
▶ 英文原文 ⏱
And conversely, what are the job functions in the enterprise that are sort of maybe administrative is too bleak, but they are supporting other functions, legal, HR, finance, et cetera. We really think that the kind of rational architecture and the one that is emerging is that for jobs that have unlimited upside, like sales or product, you always want to use Frontier tokens. And the reason for that is you just don't know what the value of the new product feature or closing a customer account is. It's effectively unbounded.
因此，从经济角度来讲，为了得到一个哪怕只是多出一个智商点数的模型，支付几乎任何价格都是合理的。比如说，为了获取更智能的Fable 5、GROC或GPT 5-6。然而，当你谈到诸如金融这样的领域时，最好的方式是准确地结算账目。你不可能做到比“准确”高出十倍。所以，你在这里会遇到一个上限问题，这就是为什么在这种情况下使用具有强化学习的开放权重模型是合理的，因为它能实现一个帕累托有效的成本曲线。
▶ 英文原文 ⏱
And therefore, it's economically rational to pay almost any price for a model that's even one IQ point smarter. You know, your Fable 5 or your GROC or your GPT 5-6. Conversely, when you talk about something like finance, the best way to close the books is accurately. You can't close it, you know, 10x better than accurately. So as a result, you kind of have this bounded upside problem where it makes sense to use open weight models with reinforcement learning for the kind of Pareto efficient cost curve.
在我们结束这个话题之前，因为这是一个很有意思的争论。当几周前Kimmy出现时，这个话题引起了很多烦恼，主要集中在相对成本上，这是讨论的焦点。但是，我们公司的创始人，Decagon的Jesse Zhang，发布了一篇很不错的帖子。他指出，对于像Decagon这样的公司来说，开源在某些方面实际上是唯一的选择。原因不仅仅是成本问题，还因为他们能够对其进行本地化、培训和微调。
▶ 英文原文 ⏱
And maybe before we go off this one, because this is a great debate. And again, when Kimmy dropped a few weeks ago, there was a lot of consternation about this topic, just given the relative cost, which was the focus of the topic of discussion. But, you know, our founder, Jesse Zhang, from Decagon dropped this great post around the fact that in some respects, and for a lot of companies like Decagon, open source is actually the only option. It's not just cost, it's that they can actually localize it, train, fine-tune it.
当然，我们可以详细探讨一下这种配置，讲讲其中的细微差别，以及为什么人们不应该为此担心。即便在创业公司的背景下，围绕这个主题实际上有很多充足的资源。当前，我们注意到的一个重要话题或趋势是，不同的模型有各自的比较优势。这些模型通常关注的领域会产生一定的紧张关系。例如，有一类模型具高度字面理解能力，GLM-52 和 GLM-53 是这类模型的典型例子。这些模型就像是某种自闭类型，它们非常字面化，只会严格按照指令执行，即只做你告诉它们的事情，绝对不会多做。
▶ 英文原文 ⏱
And so maybe unpack a little bit of that configuration, talk through the nuances there, and why folks shouldn't be concerned, even though that is the case for startups, that there's a lot in the way of abundance around this topic. Yeah, I mean, one of the big topics that we're seeing, or one of the big trends, is that there are just one, there are sort of comparative advantages of different models. So, and the models often have sort of areas of focus that are almost at tension with each other. So you see a certain set of models that have a high degree of neuroticism, like they're sort of autistic models, GLM-52 and GLM-53 are great examples of this, where they're very literal and they'll only do exactly what you told them to do and nothing more.
然后，我们看到像 K3 这样的模型，它们更加开放，非常自信且具有创造力。在组织中，对这两种类型的模型都有规则，并且这两种心态往往存在冲突。这就是为什么你实际上需要多种模型的一个原因。强化学习是一个非常重要的点。也就是说，如果你有一个可以围绕问题进行专门化设计的模型，并结合你的推理过程，你可以在你的领域为你的客户群创造出一种积累的优势，从而能够将这种智能塑造得比任何一般智能更适合你的问题。我知道 Harvey 在这方面也取得了一些很好的成绩。
▶ 英文原文 ⏱
Then we're seeing models like a K3 that are just much more sort of open and they're very presumptuous and they're creative. And there are rules for both types of models in the organization and often the sort of shapes of those minds, if you will, are at odds with each other. So that is like one reason you actually want to have multiple models. The reinforcement learning is a really important point. you know, if you actually have a problem that you can specialize the model around with your reasoning traces, you can start to create this compounding advantage in your domain for your customer base, where you're able to kind of shape the intelligence to be better than any general intelligence for your problem. I know Harvey's had some great results with this as well.
现在，这种强化学习的权衡是你会失去通用性。因此，如果你有一个经过精细调整的模型来解决法律问题，它可能在解决理论数学问题方面就不那么出色了。而这对于Harvey的应用或者Decagon在客户支持方面的应用是可以接受的。这种开放式权重的专业化特性非常独特，也是我们的初创公司选择它们的原因之一。这也是一个热门话题。自从1月份以来，我们学到了很多东西。我们真的应该每月甚至每周都来讨论这个话题，因为变化实在太多了。
▶ 英文原文 ⏱
Now, the trade off of that kind of reinforcement learning is you lose generality. So if you have the best sort of model that's fine tuned for solving legal problems, it may not be great at solving sort of theoretical math problems. And that's okay for Harvey's uses or in the case of Decagon, customer support. So this sort of open weight specialization property is something that's very unique and one of the reasons our startups are selecting them. This is also a big topic. We've learned so much since January. We should really do this monthly, Jen, or I mean, honestly, weekly. There's just so much changing.
在一月和二月的时候，有很多讨论，内容非常独特且有趣。你知道吗，Anthropic Quad发布了一种称为法律插件的东西。插件其实就是一些技能文件的集合，你可以把它想象成一个技能文件的压缩包。技能文件实际上就是提示，它们其实只是很长的提示。然后出现了一场巨大的恐慌，汤森路透和其他一些大的法律公司股票突然大幅下跌。但这些所谓的插件其实只是一些提示而已。有很多讨论在于这些实验室是否会垂直整合到应用层上。相反，我们看到的是完全不同的一幕，他们确实在垂直整合，但方向是往下，整合到推理和计算层面。
▶ 英文原文 ⏱
So in January, February, there was a lot of discussion and it's very idiosyncratic and interesting. You know, Anthropic Quad released what is called a legal plugin. You know, plugins are just collections of skill files. You can think of it as a zip of skill files. Skill files are just prompts. They're just long prompts. And there was this huge panic and all of a sudden, Thomson Reuters and a bunch of other sort of, you know, big legal names traded down dramatically. But those were really just prompts. And there was a lot of discussion about if labs were going to integrate, vertically integrate up into the application layer. Instead, we've seen the very opposite, which is, yes, they are vertically integrating, but they're vertically integrating down into inference and compute.
从事后来看这其实是合乎逻辑的，因为推理的工作量非常相似。因此，你可以在价值链的一部分实现巨大的规模。而当你考虑应用层时，就会发现有许多独特的要求和特性，比如定价、包装、产品化以及市场购买方式等。因此，进入应用层相较于推理层，是一个更加具有挑战性且运营成本高的选择。这正是我之前提到的关于模型商品化的讨论。如果你每天都在使用这些模型（就像我一样），并努力在每次新模型发布时做一些大或小的事情，你就会开始意识到这些东西并不是简单的商品，它们在特定领域具有比较优势。
▶ 英文原文 ⏱
It's actually logical now in hindsight because the workloads for inference are very homogeneous. So you can build enormous scale in one part of the value chain. Whereas when you think about the application layer, you know, you've got so many idiosyncrasies and unique needs in terms of pricing, packaging, sort of productization, how the market wants to buy. So it's actually a much more challenging and OpEx heavy proposition to move into the application layer versus moving down into the inference layer. And this is the point I alluded to earlier, which is sort of this discussion of model commoditization. You know, if you use the models every day, which I do, I sort of hold myself to a standard of making something either small or big with every model that comes out, you start to appreciate the fact that these things are are not commodities, that they have comparative advantage at a domain level.
一个很好的例子就是OpenAI的最新GPT模型，它们在知识工作方面表现得非常出色。这样的工具也非常适合进行知识工作。你应该知道，如果你用过ChatGPT的桌面应用程序，就会明白我所说的。如果你还没用过，请安装一下，真的很酷，很有趣。我所说的“工具”有点像产品容器，就像浏览器一样。它是处理电子表格、幻灯片演示、文档编写等各种工作的理想产品容器。谈到许多人可能熟悉的Cloud Code，它则完全是为软件工程量身定做的。从其终端用户界面到细小的设计决策，再到它专注的领域，比如代码规划和代码测试，都是面向软件工程师的。
▶ 英文原文 ⏱
So a great example is OpenAI with their new GPT models are just so, so good at knowledge work. The harness is also very well set up for knowledge work. You know, if you've used the chat GPT desktop app, you know what I mean? If you haven't, please install it. It's very, very cool and interesting. And it's the perfect sort of when I say harness, I kind of mean kind of product container like a browser. It's the perfect product container to do spreadsheets and slide presentations and written documents and all of that type of work. If you look at cloud code, which many of you I'm sure have used, it's just so oriented towards software engineering. You know, it's in a terminal UI, everything from the small design decisions to the areas in which it specializes, like code planning and code testing is oriented towards the software engineer.
两个产品在各自的专业化方向上都有很多权衡取舍。首先，这种领域级别的专业化已经在发生。其次，就像前面提到的，我把这看作是类似于“大五人格特质”的情况。如果有人研究过这个，你会知道一个人不可能同时既非常开放又非常神经质。有时候，当你在解决会计问题时，需要神经质特质，而在处理设计问题时，则需要开放性。因此，在组织中实际上需要两种不同类型的思维，这也是为什么你可能会选择使用GLM-53而不是Kimi K-3。因此，我们认为这绝对不是一种商品化的选择。
▶ 英文原文 ⏱
And there are many trade offs both products are making for that sort of respective specialization. So one, you've kind of got this domain level specialization that's already occurring. And then two, as I mentioned earlier, you've got the sort of I think of it as the big five sort of personality traits. If folks have studied that, you know, you can't be both highly open and highly neurotic. And, you know, sometimes when you have an intelligence, you're applying to an accounting problem, you want neuroticism when you're applying it to a design problem, you want openness. So you actually have a need for both types of mines in the organization, which is why you would select something like a GLM-53 versus a Kimi K-3. So definitely not commodities in our view.
这是一个重要的观点。你知道，在许多产品类别中，模型汇聚带来了更好的效果。一个很好的比喻就是Expedia。使用Expedia比分别去United、Delta或Southwest网站要方便得多，因为你希望在一个地方就能查看所有航空公司的库存。类似地，在编程中，我们看到很多像Cursor这样的应用，你可能希望使用一个前沿的模型来进行规划，但在执行时可以使用较简单的模型。所以，你确实需要一个能够整合使用多种模型的产品架构。
▶ 英文原文 ⏱
This is an important point. You know, there are many product categories in which model aggregation delivers a greater than sum of parts outcome. And, you know, a good metaphor for this is Expedia. You know, it's so much more useful to use Expedia than it is to go to United than to go to Delta than to go to Southwest. You just want a single place where you can benefit from seeing every airline's inventory. Similarly, you know, in coding, we're actually seeing this with Cursor a ton where you want to do a very frontier model for planning, for example, but then you can use a lesser model for execution. And you really need to have one product harness or sort of product architecture that lets you use multiple models.
创意工具是一个很好的例子，你知道，有专门针对不同模式的模型。例如，你有像Eleven Labs这样的平台，当然它在语音音乐方面表现出色。然后你还有像Black Forest这样的公司，在视频和创意指导方面做得非常出色。正确的产品策略是将所有这些整合到一个整体中。最后是研究和决策。我们经常看到这种情况，模型通常是通过不同的数据集进行训练的，没有重叠。因此，通过将同一个查询在多个模型中对抗运行，你能够获得更多的信息，然后再通过一个独立的模型帮助你收敛这些信息。
▶ 英文原文 ⏱
Creative tools is another great example where you've got, you know, models that specialize in different modalities. So you've got something like an Eleven Labs, which of course is incredible at voice music as well. And then you've got something like Black Forest, which is doing such an excellent job in kind of video and creative direction. And the correct product is to bring all of these together into one shell. And then finally, research and decisions. We see this all the time where, you know, the models are trained with sort of non-overlapping data sets often. So you're able to just get more information by running the same query through many models adversarially and then having a separate model sort of help you converge.
这是一个应用层大放异彩的地方，因为实验室当然会受到激励而仅能提供自己的内部模型。作为一个应用的聚合者，你可以提供最优选择。好了，我们来进入应用层。关于应用层的关键点是，智能就像购买云服务一样，是一种基础工具。Salesforce是如何运作的呢？它利用AWS的云服务，将其转化为可以为所有客户群体实现经济效益的CRM软件。AI应用层也是如此，虽然拥有原始的智能工具很棒，但你真正需要的是像Harvey这样的工具，将其转化为法律行业的经济效益。
▶ 英文原文 ⏱
This is a place where the application layer really shines because labs, of course, are both incentivized and structurally only able to provide their own in-house models. You as an application sort of aggregator can provide the best of breed. Okay, let's jump into the apps layer. Now, the key point about the application layer is that, you know, intelligence is a primitive, just like buying cloud is a primitive. And what does Salesforce do? It sort of takes the, you know, AWS cloud primitive and turns it into CRM software that delivers an economic outcome for all of their customer segments. The same thing is true of the AI application layer. You know, it's great to have the raw intelligence primitive, but you really need Harvey to turn that into an economic outcome for the legal industry.
对于像信用合作社这样的人来说，这是一个非常有趣的市场细分，因为他们在购买产品、产品形式化以及市场愿景方面都有独特的需求。你知道，大多数信用合作社并不希望将员工数量减半，而是希望数量翻倍，对吧？而且他们希望在经济表现良好的情况下实现这一目标。因此，在他们的市场细分中，他们对智能基础的运用有着非常具体的看法。而应用层的机会就在于能够满足这种需求。
▶ 英文原文 ⏱
Similar for somebody like credit unions is a really interesting market segment where they're so idiosyncratic in how they want to buy products, how they want the product sort of productized and the shape of the ambition for their market. You know, most credit unions don't want to decrease their headcount by half. They want to double it, right? And they want to double it while having an economically performant business. So it's just a very specific way that they see the intelligence primitive playing out in their market segment. And the application layer's opportunity is to be the one that kind of delivers that.
这是一个有点高级的概念，但我认为很重要。回顾一下人工智能使用的发展历程，可以看到它已经从简单的模型提示发展到将模型放入循环中。虽然"代理"这个词被用得很多，但其实就是在循环中加入工具、记忆和其他一些元素的模型。这方面的一个好例子就是编码。我们可以看到软件公司通常的做法：一个错误被记录下来，然后被重复出现，接着解决方案被生成，再进行验证。如果这是一个低风险的修复，就会被整合进系统并发布。可能客户还会收到一封邮件，告知他们的问题已经修复。
▶ 英文原文 ⏱
This is a bit of an advanced concept, but I think an important one. If you look at the kind of way that the evolution of AI use has gone, it's gone from prompting models to putting models in loops. You know, the term agent is overused, but agent is just a model in a loop with sort of tools and memory and a few other things. A great example of this is coding. You know, we've all seen this from software companies, which is a bug gets recorded. It gets reproduced. A fix gets generated. It gets verified. If it's a low risk fix, it gets integrated and shipped. And maybe the customer gets an email saying your bug was fixed.
如果这是一次高风险的变更，也许需要人来审核。但这样一来，每一个实际报告给企业的漏洞都会通过这个编码循环自动修复。当你开始把这个想法应用到业务的其他部分，比如价格优化和采购时，这些都是非常自然的业务循环，可以由这些模型完全自动化。最有野心的循环可能就是业务循环了，比如你做了一个对整个业务都有影响的变更，而模型反馈说：“嘿，我觉得我们需要在蒂华纳开一个分店。”
▶ 英文原文 ⏱
If it's a high risk change, perhaps a human reviews it. But that way, every bug that actually gets reported to the enterprise now gets autonomously fixed through this coding loop. As you start to take that idea and apply it to other parts of the business, things like price optimization, things like procurement. These are very natural sort of business loops that occur that can be fully automated by these models. And then perhaps the most ambitious type of loop is the business loop, which is, hey, you make a change that's very cross-cutting to the business. And the model comes back and says, hey, I think we need to open a branch in Tijuana.
现在，模型还不能自主完成这件事，但它可以在整个业务的表层做出改变，这是非常了不起的。这就是企业自动化将通过人工智能实现的方式。我觉得，对于我来说，编程一次又一次地成为一种例证。而法律也是另一个优秀的行业领域，而非仅仅市场。这是马克所说的，他的观点非常正确，因为如果你把智能看作一种原始元素，不妨现在将编程智能也视作一种原始元素。所有这些产品都在各自的技术栈领域中工作。你知道，quad code 在某种程度上很好地向开发人员展示了底层硬件，而 Replit 则为那些不熟悉代码的小型企业主提供了一个很好的抽象层。这些都是对编程和智能的定价、产品化、包装的各种变化，而它们都因此在运作。
▶ 英文原文 ⏱
Now, the model can't do that autonomously, but it can make a change at the sort of surface level of the entire business, which is extraordinary. This is how enterprise automation is going to occur through AI. And I think, for me, coding has just been over and over again an illustration. And legal is another great area of industries, not markets. This is something that Mark says, and he's so right, which is if you look at intelligence as a primitive, let's think now about coding intelligence as a primitive. All of these products are working in the sort of respective areas of the stack. You know, quad code is such an excellent job of kind of exposing the raw hardware, so to say, to the developer, all the way up to Replit, which is a great abstraction layer for the average small business owner that's unfamiliar with code. These are variations of sort of pricing, producting, productization, packaging for the coding primitive and intelligence, and all of them are working as a result.
我认为我们需要进行一个重要的思维模式转变，即确保我们将市场评估为各个行业，而不是简单的市场。好，现在说说消费者。消费者在过去几周的表现非常出色。我们已经说了三年，这将是消费者的季度，我认为这次可能真的是消费者的季度。让我们深入探讨一下。到目前为止，阻碍消费者的因素有几个。首先，消费者不太愿意为软件买单，我们一次又一次地学到了这个教训。不幸的是，与过去软件的“魔力”不同，人工智能软件在分发和使用上有边际成本，而且这些边际成本有时会非常高。我开发了一款帮助我浏览 X 时间线的应用，每增加一个用户的成本是 250 美元。如果我是一个初创公司的创始人，看到这样一个 250 美元的成本，即使用户获取成本是 0 美元，也很难让一个针对大众市场的免费产品成功。这种情况正在发生变化，因为开放模型的出现使得产品运行成本大幅降低且表现更佳。
▶ 英文原文 ⏱
So I think a big mental model shift for us is ensuring that we're assessing these as industries, not necessarily simple markets. Okay, and consumer. Consumers had a really cool couple of weeks. You know, we've been saying for, you know, for three years that this is going to be consumer's quarter, but I think that this might be consumer's quarter. Let's go into it. The things that have actually held back consumer so far have been a couple of things. You know, the first is consumers don't love paying for software. We've learned this lesson over and over again. And unfortunately, unlike the sort of magic of software in the past, AI software has marginal costs of distribution and engagement. And the marginal costs can sometimes be very high. You know, I built an app I use to help me browse my X timeline, and it costs $250 to onboard a new user. So if I'm a startup founder looking at that, looking at a kind of $250, even with a $0 TAC onboarding cost, it's very hard to make a mass market free product work. That is changing now because of open-weight models, dramatically cheaper and more performant.
你知道，第二个方面是我们从未有过一个专为AI设计的分发渠道。AI没有类似应用商店的平台。因此，对于消费者来说，这个产品周期更像是Web 2.0，你需要在构建产品的同时也建立起分发渠道，而不像移动互联网那样有一个整个生态系统的中心分发点。最后一点，我认为这很重要。可以说，命令行阶段就像AI的DOS时代，为了让消费者完全接受这项技术及其能力，我们需要一个类似Windows的平台。因此，我们认为在产品和设计方面还有大量工作要做，以确保消费者知道如何使用这些神奇的新功能。
▶ 英文原文 ⏱
You know, the second is we've never had an AI-native distribution channel. There's no app store for AI. So this actual product cycle for a consumer looks more like Web 2.0, where you have to kind of build the channel alongside the product, and less like mobile, where you actually have the central point of distribution for the entire ecosystem. Then the final point, I think, is an important one. You know, command line is we're sort of in the DOS era of AI, and for this technology and its capabilities to sort of fully be embraced by consumers, we're going to need the windows, so to say. So we think there's just a ton of work to be done around product and design craft to ensure that consumers know how to consume all these magical new capabilities.
有两件事正在发生变化：编码代理非常出色，我知道这个话题已经被讨论过。我认为有趣的是思考它们如何为消费者服务。你知道，如果你想到数字化原住民企业家的概念，如果你不是程序员，过去的表现方式就是成为一个YouTube创作者。大约十年前，我们经历了一次关于孩子们想当YouTube创作者而不是宇航员的道德恐慌。但我认为应把这解读为在互联网环境中长大的孩子们实际上希望在互联网上创业，而唯一的实现方式就是成为创作者。现在，有了编码代理，你可以打造一个每年产生十万美元甚至一百万美元收入的软件产品。这些企业虽然不具备吸引风险投资的条件，但它们是一种夫妻店形式的软件即服务（SaaS）机会，这种机会正在出现，我觉得对国家来说非常酷。
▶ 英文原文 ⏱
Two things are working. So coding agents are extraordinary, I know have been discussed. I think it's interesting to think about how they work for consumers. You know, if you think of this concept of the digitally native entrepreneur, if you're not a programmer, the way that's historically shown up is you're a YouTube creator. And there was a whole moral panic that we had, you know, 10 years ago about the kids want to be YouTube creators, not astronauts. But I would interpret that instead as the kids actually who grew up on the Internet want to build businesses on the Internet, and the only way to do it, again, is being a creator. Now with coding agents, you can build a software product that generates $100,000 of revenue a year, $1 million of revenue a year. Now, these are not venture-backable businesses, but it's a sort of mom-and-pop SaaS opportunity, which is emerging and I think very, very cool for the country.
个人代理，我们在一月份对OpenClaw有过一阵集体的兴奋，那是一种非凡的原始组合，但它从未真正进入消费市场。你知道，它有点像是开发者导向的东西，更像是家酿计算机俱乐部的那种氛围。随着GrokBot和ChatGPT的出现，我们开始看到个人代理正在被转化为消费者可以使用的软件。Anisha，在我们进行城镇演示之前，你介意稍微暂停一下吗？因为你是上一个消费应用体验时代的创业者，每当我想到这个问题，我都会想，天哪，如今我们该如何定义消费者呢？因为，比如说，现在有水管工利用GrokBot彻底改造他们的业务，从头到尾，这算是消费者领域还是企业领域呢？因为这很像是由产品驱动的运动，但其起点是从消费者转变为企业的。
▶ 英文原文 ⏱
Personal agents, we had this collective moment of excitement around OpenClaw in January, and it was an extraordinary sort of composition of primitives, but it never really crossed over into consumer. You know, it was sort of a developer-oriented thing, more of the homebrew computing club kind of energy. We're starting to see, with the emergence of GrokBot and ChatGPT work, personal agents being turned into software that consumers can use. Anisha, actually, do you mind just pausing on this before we go to the town demo? Because, you know, you were a founder building in the last era of the consumer app experience, and when I even think about it, I was like, gosh, how do you even define consumer today? Because, you know, the plumber that utilizes now GrokBot to completely turn around their business end-to-end, like, is that consumer or is that enterprise? Because, like, it's very, like, it's almost like a, like, PLG-led movement, but it's coming from, as a consumer, that then's crossover into enterprise.
最近一段时间的消费型应用更倾向于通过娱乐来实现盈利。因此，我们可以深入探讨这个趋势，特别是在你参与其中的部分。我们的简单原则是，如果你不能通过销售来吸引客户，也就是说通常需要平均客户价值达到1.5万美元，那么你就需要通过营销来获得客户。我们把这些客户看作消费者，其中大多数是小企业主。因此，在我们的投资视角中，水管工绝对是消费者。娱乐业很大，并且将会出现许多以人工智能为核心的娱乐公司。我认为Character是一种娱乐公司。在亚洲，短篇戏剧已经成为一个很大的趋势，现在也开始逐渐影响其他地区。许多短篇戏剧是生成式或依靠生成技术辅助制作的。所以，我认为娱乐行业将会非常庞大。大多数人想要花费时间，而不是节省时间。消费者对效率提升并不太感兴趣。这些变化是势在必行并且值得深入研究。
▶ 英文原文 ⏱
And particularly, like, the last era of consumer application is more towards entertainment as a way to monetize. And so maybe unpack some of that, and particularly where you've been spending time as a part of that. I mean, our simple rule is, if you cannot justify acquiring the customer through sales, which usually means a 15K ACV, you have to acquire them through marketing. We think of them as a consumer, which is most small business owners. So I think that the plumber is definitely the consumer in our sort of investing mind. Entertainment is huge, and there's going to be a bunch of AI-native entertainment companies. You know, I would argue Character was kind of an entertainment company. There's been a huge trend around short-form drama, mostly in Asia, and that's starting to come over here. Many of those are generative or sort of generative-assisted. So, look, I think entertainment is going to be massive. Most people want to spend time, not save time. The consumer is not that interested in productivity. So that's definitely going to happen and probably worth a separate deep dive.
好的，Tom，我认为，对于那些使用过这个产品的人来说，它真是一种神奇的体验。你知道，这几乎是我给所有人的首要建议——朋友、家人、行业内的人——那就是，请一定要去使用这些产品。因为当你看到它们如何在日常中变化时，建立直觉真的很容易。我们投资了一个叫Talent的产品由我们的合作伙伴Alex Rampel创立。这是一个非常出色的生产力工具。你会看到产品随着时间的推移，通过记忆的优势，如何不断改进。当你第一天使用产品时，它对你了解得不多，就像一个刚上岗的新员工，需要时间来熟悉情况。到了第30天，它已经能够为你做出很好的假设，因为它已经积累了30天的上下文、记忆和技能。这是我们越来越多地看到的一种模式，产品为最终客户持续提供复合价值，这表现为业务上的客户留存率提高，以及对每位客户的定价权增强。
▶ 英文原文 ⏱
Okay, and I think, Tom, for folks who have used it, it's just such a magical experience. And, you know, this is, like, the number one sort of piece of advice I give to everybody, friends, family, folks in the industry, is, like, please just use the products. Because it's so easy to build intuition when you see how they change day-to-day. And Talent is an investment our partner, Alex Rampel, made. It's a really extraordinary productivity product. And you sort of see how the compounding improvement of the product through memory advantages it over time. So the first day you use a product, it doesn't know you that well. It's sort of like an employee, a new hire who's just getting up to speed. By day 30, it's able to make excellent assumptions on your behalf because it just has soaked in 30 days of sort of context, memory, and skills. And this is a pattern that we're seeing more and more, the sort of compounding value being delivered to the end customer, showing up as retention in the business, and sort of showing up as pricing power on a per-customer basis.
是的，这是一个很棒的工具，因为人们可以利用Talent来满足个人需求。而且，这是一个免费的试用，我记得，他们会给你大概40个积分作为开始。所以，当你把它连接到你的个人邮箱时，就可以大致看看它的效率有多高。在工作方面，我的邮箱始终是空的。但在个人方面，我的收件箱里大约有2万封邮件。大卫·乔治可能对此感到无奈，因为这样实在不可接受。不过，在个人生活中，这种情况是常见的。所以，如果你发邮件到我的个人邮箱，我可能永远不会回复。然而，我把Town连接到我的个人邮箱，现在几乎不再去查看它了。如果有重要的事情，Town会提醒我。它还会清理订阅邮件和能进行优化的其他邮件。而且，它开始自我改进，比如会发邮件告诉你，“这项操作花费了多少”，并告诉你如何节省积分。所以，它在生产力方面是很有突破性的工具。
▶ 英文原文 ⏱
Yeah, this is a great one because folks can utilize Talent for their personal use case. And it's a free, you know, trial. They give you, I think, something like 40 credits to start or something around there. And so you can kind of see it once you plug into your personal email how productive it actually is. On the professional front, I'm always inbox zero. On the personal front, my inbox is like 20,000. David George is probably cringing on the inside here just because that is unacceptable. However, you know, personal life, things are common. So if you email me on my personal, I will never respond to you. However, I plug Town into it and like, I don't even check it anymore. If there's something important, Town will surface it to me. And also, it does all the scrubbing of, like, subscriptions and all the things that it can optimize. And it's starting to now self-improve upon itself. So, like, it'll send you emails where it says, like, hey, this routine is costing this much. Like, here's how you could actually save your credits by this. So, it's sort of this unlock into what starts on the productivity side.
您说得对，也许一开始人们不会愿意为此买单。但是一旦这个服务被固定下来并且在权限上有所扩展时，人们可能会觉得，好的，我愿意花一些钱，因为这有助于管理我的生活，我可以把它设置为自动运转。这确实是个很好的观点，Shannon。我的思维模式就像是老员工和新员工的比较。新员工可能很优秀，甚至比老员工成本更低。但是我们都知道老员工的价值，他们能够为组织和我们做出很好的判断。
▶ 英文原文 ⏱
And to your point, maybe people won't pay for that personally. But once it starts to get locked in and then expand in terms of the remit, you're like, okay, I'll pay the whatever X bucks, you know, just because it helps to manage my life. And I can put it on autopilot. Yeah, it's such a great point, Shannon. Like, my mental model for this is just an experienced employee, a tenured employee versus a new hire. You know, the new hire may be brilliant. It may even cost less than a tenured employee. But we all know the value of a tenured employee. They're just able to make great assumptions on behalf of the organization and you.
这有点哲学化，但我认为这就是事情的发展方向。就像我们之前谈到企业的代码循环和业务循环一样，我们认为在消费者的生活中也存在一系列非正式定义的循环。这些循环涵盖了家庭、友情、金钱、健康等领域。在这些领域，你会不断交换信息、做出决策、行使自主权、执行计划，然后循环继续。因此，我们开始看到一些围绕自我提升的循环出现，其中健康和财务是OpenAI关注的两个领域。
▶ 英文原文 ⏱
And, you know, this is a little philosophical, but I think this is where it all goes. Just as we talked about kind of coding loops and business loops for the enterprise, we think there's a set of loops that are informally defined that really sort of lay out a consumer's life. Think of family, friendships, money, health. These are all areas where you're sort of changing information, decisions, agency, execution, and then the loop continues. So we're starting to see some of these sort of loops emerge around self-improvement, kind of health and finance are the two areas that OpenAI is focused on.
我们看到有很多初创公司在致力于购物领域的创新。但我们认为，这种情况的发展最终会大幅提高消费者的生活质量。这与过去的产品周期类似，其中80%的收益最终传递给大众市场。Anish，你觉得，抱歉，我想回到上一张幻灯片，在这儿有个问题。你怎么看这些个人助手的例子，比如town或ethos等，是不是都指向一种有情境感的助手呢？
▶ 英文原文 ⏱
We've seen a bunch of startups working on shopping. But we think that, like, the kind of way that this ends up playing out is a dramatic quality of life improvement for the consumer. And that really follows the shape of past product cycles where 80% of the surplus is delivered to the mass market. Do you think, Anish, that all of these, sorry, maybe just going back to the last slide, there's a question here. You know, when you think about these personal agent examples, whether it be town or ethos, et cetera, all point to, you know, kind of one assistant having context.
但似乎有很多不同的选择。你认为最终会出现一个主导平台来管理个人生活中的时间管理吗？还是会像操作系统那样，有许多平台互相交流，并在后台进行配置？让我想到比较优势的观点。我认为，你对财务顾问的要求和对派对策划师的要求是不同的。
▶ 英文原文 ⏱
But it seems like there's many different options. Do you think it'll end up being, you know, sort of one dominant platform for this personal aspect of your life as time management? Or will it be like an operating system where you have many kind of talking to each other and kind of configuring on the back end? The comparative advantage point kind of comes to mind. You know, I think the characteristics you want from your CFA are different from the one that you want from your sort of party planner.
这段英文的意思是：
我认为情况是这样的，表面面积非常广阔，所以是的，确实可能存在重叠的背景。我觉得GrokBots在产品中很好地展示了这一点，你有许多指向略微不同方向的机器人，它们都协同工作以传递一个全局最优的结果。这里有几个问题，我会回到你之前讨论过的话题。所以，如果应用层捕获了经济成果，你怎么看待来自模型公司的竞争？
▶ 英文原文 ⏱
And that just the surface area is so broad that I think that, yes, there's overlapping bits of context. I think GrokBots has done a nice job of kind of illustrating this in product, where you have many bots that are pointed in slightly different directions that all coordinate to deliver a globally optimal outcome. There's a few questions. I'm going to go back to topics you've covered earlier. So if the application layer captures economic outcomes, how do you think about the competition from the model companies?
它们将如何允许价值在下游增加？而且，你知道，在应用层面的公司能否与那些专注于特定市场的前沿实验室竞争？我认为可以。再次强调，我觉得我们低估了产品定价、包装及终端客户购买方式的复杂性。你知道，一名青少年想要获取智能技术的方式和信用社的市场主管实际希望获取智能技术的方式是不同的。
▶ 英文原文 ⏱
And what will they allow value accretion to happen downstream? And, you know, are companies at the app layer able to compete with the Frontier Labs going after that particular market? I mean, I think so. Again, I think that we're underestimating the kind of complexity of product pricing, packaging, and how the end customer wants to buy. You know, the way that, you know, a teenager wants to consume the intelligence primitive is different than the way a marketing executive at credit union wants to actually consume it.
嗯，对我来说，这种情况非常多样化，让实验室向上移动到应用层还不如向下移动到推理层更有意义。所以，你知道的，那种权限问题是很有趣的。我想，如果我们生活在2023年的一个世界中，一个模型能够统治一切，那么即便你有权限也无关紧要，因为实验室最终会逐步拿走你所有的毛利。
▶ 英文原文 ⏱
Um, and it's very heterogeneous, to me, it just makes less sense for, um, the labs to move up to the apps layer than to move down to inference. So, you know, and that kind of permission point's an interesting one. I think if we lived in a world of 2023, when it was one model to rule them all, it wouldn't even matter if you had permission because the labs would just take a hundred percent of your gross margin over time.
但现在，由于在帕累托前沿的每个点上都有很多选择，实验室实际上更难做到这样的事情了。太好了。刚刚有个关于进展的问题。那么，你们会资助那些目前还没有收入的项目吗，考虑到人们取得进展的速度如此之快，还是这非常困难？我们尽量不这样做，我确实在这方面花的时间更少。
▶ 英文原文 ⏱
But now, because you've got many options at all points in the Pareto Frontier, you know, the labs have a harder time actually doing things like that. Awesome. There was a question just on traction. So do you fund anything where there's, um, there's no revenue at this point, just given how quickly people have been making progress or is it extremely difficult? Um, we, we try not to, I, I certainly have spent less time, um, on that strategy.
我觉得，在这个投资组合中，大多数项目从产品开发速度的角度来看，确实显示出了一些成果。过去，我们对此有严格衡量，比如，现在在任何阶段的路演中没展示出真实产品几乎都是不合格的，因为现在开发东西非常简单。所以我们看到几乎所有项目都展现出了一些突破的迹象。
▶ 英文原文 ⏱
Look, I, I think that the basket is majority investments that are showing some signs of working, certainly from a product velocity perspective. That used to be something we measured pretty carefully, like it's a disqualifying to not be showing a live product in a pitch at any stage these days, because it's so trivial to build stuff. So almost everything we we're seeing are showing signs of, you know, sort of some sort of breakout.
我的意思是，我的模型有点简单化，我只是看看一旦你有了统计数据、销售和产品，我们可以从那里进行推断，嗯，我们是否喜欢为参与其中而需要付出的价格和我们隐含承担的风险。我会说，这就是我们大部分工作的重点，我们寻找非常有才华、有经验的人。我们确实会做一些小规模的选择，就像在所有事情开始之前的一轮投资。但这不是我们主要的工作内容。
▶ 英文原文 ⏱
I mean, my model is somewhat simplistic where I just sort of look at once you have stats, SIG sales and product, if we extrapolate from there, um, do we kind of like the price that we have to pay to be a part of it and the risks that we're taking implicitly. And that's, I'd say, the majority of the work that we do look for very talented, experienced folks. We do kind of take a small call option, which looks like a pre everything round. Um, but that's not the majority of what we do.
好的。但是，当你考虑到这个竞争环境时，多年来消费者一直未受到重视。现在你是否看到了一种转变，因为显然应用程序已经成为下一个价值创造层。虽然模型层在某种程度上已经确立，但我得说这话时是有保留的，因为可能会有新的算法突破，或者像我们投资组合中一样，突然冒出一些意料之外的创新者。
▶ 英文原文 ⏱
Yeah. Yeah. But when you think about the, the, um, kind of competitive landscape on this, uh, consumer has been unloved for so long. Um, are you seeing now this reversion, just given it's clear that apps is sort of this next layer of value creation, like the model sort of layer has been somewhat set. And I say that with a huge asterisk because there might be new algorithmic breakthroughs, you know, kind of kind of folks coming out from left field as we have in the portfolio as well.
嗯，你是否觉得竞争动态确实已经更多地转向应用层面了？百分之百是这样的。我的意思是，对于那些致力于消费领域的人来说，这简直是一个复兴的时代，因为你可以使用一些非常出色的基本工具。而且顺便说一下，我们现在有了一种能够在情感和人际关系领域运作的基本工具。比如，你可以和云服务、OpenAI或者K3进行交流，并感受到情感。
▶ 英文原文 ⏱
Um, but do you feel like the shift from the competitive dynamics shifting more towards application? A hundred percent. And I mean, it's sort of a Renaissance for being a consumer builder because you've got this extraordinary primitive that you can work with. By the way, we now have a primitive that can kind of operate in the, you know, emotional interpersonal domain. You know, you could like have a conversation with cloud or open AI or K3 and feel feelings.
在过去的40年里，科技发展极大地提升了我们的智力和生产力，但却没有任何东西真正触及我们的人性。这是一种完全不同的技术层面，范围非常广。我认为有一类产品，是实验室文化和大型科技公司并不擅长追求的。想象一下，比如在谷歌推出一款可能会与你意见不合的伴侣产品，甚至可能带有性暗示。
▶ 英文原文 ⏱
And we've had 40 years of technology that really boosted our intellect and productivity, but nothing that kind of spoke to our humanity. So it's a whole different technology surface. It's very wide. I think there are a set of products that labs are just culturally not set up and big tech not set up to go after. You think about launching, you know, a companion product at Google that may disagree with you, that may have sexual innuendo in it.
像这样的事情，谷歌有成千上万个委员会来预防。因此，初创公司在某些领域具有独特的能力。最后，消费者也很乐于下载新软件，并为之付费。这就像2009年的圣诞节，大家对新推出的iPhone应用程序充满期待。但与当初0.99美元的时代不同的是，现在人们愿意每月支付200美元。因此，这对于消费者开发者来说是一个复兴的时期。
▶ 英文原文 ⏱
Like these are the things that there's a thousand committees that Google, um, are designed to prevent. So startups have areas where they're kind of uniquely capable. And then look, finally, the consumer sort of excited to download new software, excited to pay for it. It's like Christmas 2009 with the iPhone. People want to try new apps, but unlike the 99 cents days, they're willing to pay 200 a month. So it's sort of a renaissance for consumer builders.
是的，我觉得情况已经改变了。我正尝试想个笑话。旧金山的那些活跃分子一直在耐心等待这一天，等了很长时间。呃，呃，这里有一个很好的问题，是米歇尔提出来的。我们应该如何看待人工智能应用公司的新经济模型呢？因为关于应用公司单位经济的讨论，确实很有趣。
▶ 英文原文 ⏱
And yeah, I think that things have changed. I'm trying to come up with a joke. The autists in, in San Francisco are kinly, kinly waiting for this moment down for very, a very long time. Uh, uh, there's a, there's a good, um, a question from Michelle here. You know, how should we think about the new economics of AI apps companies? Cause there's a great, there's a debate around the unit economics of, of, um, apps companies, right?
例如，他们可能有较低的毛利。他们面临更大的压力，因为他们的计算资源不如别人多。在当前的环境中，资本是一个巨大的壁垒，这使得竞争变得困难。那么你是如何考虑今天公司承保收益的经济状况的呢？我觉得大卫写了一篇很棒的文章，关于这个问题。他提到当今的利润率话题比以前更复杂。
▶ 英文原文 ⏱
Like for example, they may, they have lower gross margins. They're just getting more pressure just cause they don't have as much compute access. Um, capital is such a moat in this environment. Um, it's, it's hard to be competitive. So how do you think about the economics of, of underwriting returns in, in, um, in companies today? I mean, David wrote a great post on this. I think that the kind of the margin topic is a lot more nuanced than it once was.
我认为，在很多情况下，牺牲利润以扩大产品范围是合理的。我认为这一产品周期中非常积极的一面是，消费者的支付意愿非常强。因此，我们常常与创始人一起做的一个练习就是——比如在消费者市场上，如果20美元曾是历史上的价格上限，那么你产品中售价为每月200美元的版本是什么样的。
▶ 英文原文 ⏱
I think it's actually rational in many cases to trade away margin, to have wider product surface. I think the very positive part of what's happening in this product cycle is the willingness to pay is extraordinary. And that's why the exercise that we often do with founders is like on the consumer side, for example, is if $20 was the historic ceiling, what's the $200 a month skew of your product.
事实上，每月 2,000 美元的偏高费用是什么？就像软件界的铂金包一样。我认为我们将会有这种奢侈软件。我们已经看到了人们愿意为它付费。因此，虽然利润的话题比较复杂，但人们的支付和购买意愿比以往任何时候都高。所以，可以说目前的情况有些不确定，但我们正在考虑所有这些问题。
▶ 英文原文 ⏱
And in fact, what's the $2,000 a month skew? Like what's the Birkin bag of software? I think we're going to have this luxury software. We're already seeing willingness to pay for it. So the margin topic is more nuanced, but the willingness to pay and buy is higher than ever. So, you know, it's a little bit of fog of war, but we're, we're thinking about all those topics.
作为一个喜欢小众时尚的人，你拥有爱马仕铂金包、装裱的牛仔裤，我真的没想到你是个这么时尚的人啊。这就像是机器人在帮你提升品位，朋友。对于一个我只见过穿四分之一拉链上衣的人来说，你真是让我意外。你称他为秘密。投资者是资本的好管家。好吧，这就是我所知道的。对于一个我通常只看到穿四分之一拉链上衣的人来说，我只是说说而已。
▶ 英文原文 ⏱
A niche, dropping Birkin bag, framed jeans, like I had no idea you were such a fashionista. This is like your, your bot is helping you get up to, to seat here, my friend. Uh, for a guy I only see- You're calling him a secret. An investor is a good steward of capital. Okay. That's all that I know. For a guy I only see in quarter zip ups. I'm just saying.
嗯，好的。可能想问你一个问题，关于那些创始人。我不知道你是否还记得这段对话，大概是五年前的事。那时候，大多数创始人的背景比以前多样化，部分原因是软件和技术更加先进，比如有很多项目经理从谷歌出来创业。
▶ 英文原文 ⏱
Uh, um, okay. Maybe one question for you on just on the, on the founders. Cause I, I don't know if you remember this conversation. This was probably five years ago or so, um, where most of the founders, you saw, saw more, uh, diversity in their background in part because the software and technology was way more sophisticated. So you had a lot of program managers spinning out of Google, for example, and starting a company, et cetera.
你在今天的应用程序领域看到的是哪种类型的创始人？他们是否更偏向于技术背景或者是研究型的？还是他们是产品经理？你看到的在应用程序早期阶段出现的创始人是什么样的典型呢？确实，现在看到的创始人中，MBA背景的较少，研究型的较多。他们各自都有优势和不足。我认为，目前创始人的商业能力相对较低，但技术能力显著提高。而技术能力是所有良好成果的源头之一。商业能力可以通过学习和观察来获得，但技术能力通常不是这样。因此，我们确实看到很多技术导向的早期职业创始人。不过，他们所做的事情非常非凡，因为他们没有对可能性形成任何先入为主的观念。
▶ 英文原文 ⏱
What are the type of founders you see building in apps today? Are they, do they tend to lean, you know, more technical, more researcher derivatives? Are they product managers? Like what, what kind of archetype are you seeing at least the early innings of apps come out from the woodwork on? Yeah. Yeah. Less MBAs, more researchers, um, and they, they both have their kind of strengths and weaknesses. I think the business sophistication of the founders are seeing today as lower, but the kind of technical sophistication is dramatically higher. And the technical sophistication is kind of upstream of all the good things that happened. You know, business sophistication can be kind of taught and observed, but technical sophistication typically not. So definitely seeing a much more technical kind of earlier career founder, but the things they're doing are extraordinary because they don't have any sort of preconceived notions about, well, it's possible.
很多高级创始人未能成功跨越产品周期瓶颈的原因是，他们对技术不够了解，并且他们的想法往往根植于过去，对未来的上限有一定的限制。我认为，年轻创始人最大的优势在于他们认为一切皆有可能。在一次会议上，Ben提到，以前最大的风险在于创意过于庞大，而现在则是创意过于狭小。我觉得这很能说明不同类型的创始人特质。在类似的环境下，过去如果给创始人太多资金，可能会毁掉公司，因为创始人通常有太多的想法且过于理想化，缺乏将这些想法变为现实的能力。而现在，我们正看到一个全新的模式。
▶ 英文原文 ⏱
And so much of what holds back senior founders that don't quite get to the other side of this product cycle is, you know, they're not close enough to the technology and they've got an idea that's rooted in the past of what the ceiling is. And I think the best thing about these young founders is they assume everything is possible. You know, we are an off site where Ben was saying that the, the biggest risk in the past with the ideas were too big and now the biggest risk is that the ideas are too small. But I think that's sort of illustrative of the different founder archetypes. Yep. Yep. And maybe on that similar thread, it used to be that, that if you gave a founder too much money, it would wreck the company because the founder almost always has way too many ideas and is a visionary and doesn't have the talent to actually commensurately land with all those ideas and we're seeing a whole new paradigm on that.
也许可以多展开一下这个想法，因为它在那次外部会议上是一个很重要的主题。是的，我的意思是，这实际上是一种历史智慧。比如，为什么我们不直接给每个初创公司提供2000万、5000万或一亿美元呢？其实问题不只是风险和回报，而是这些公司通常缺乏足够有才能的人来同时处理价值2000万美元的产品。他们真的需要专注于一个想法，而资金是确保这种专注的好方式。现在我们看到的是，通过多或少的资本，你可以在产品和模式上进行不同的权衡。有些公司筹集到一亿美元，并通过专注和高效的方式使用这些资金，能够提供和使用2000万美元时完全不同的价值主张。
▶ 英文原文 ⏱
Maybe unpack that idea a little bit more just because it was such a huge theme of that off site. Yeah, I mean, for sure, this was a historical wisdom, you know, why didn't we give every seed company 20 or 50 or a hundred million dollars? You know, it wasn't just the kind of risk reward, but rather typically the constraining factor was they just didn't have enough talented people to work across 20 million dollars of product surface at the same time. They really had to focus on one idea at a time and the capital was a great way to enforce that focus. What we're now seeing is you could make different sort of product and model trade-offs through more or less capital. And there is a case for a company that raises a hundred million dollars, uses it productively and in a focused way, and is able to deliver a different value proposition than the very same team would be able to do with 20.
我认为，这个问题就像我们之前讨论利润率问题时提到的战争迷雾一样复杂。关于种子轮融资的最佳规模是多少，以及能有效运用多少资本，这是一个更加精细的问题。虽然这是因为过于丰富的资源而带来的烦恼，但相比五年前所面临的问题，我宁愿为此烦恼。那时，我们的金融科技公司因不严谨的风险评估而间接补贴了客户，我们根本不知道出路在哪。嗯，是的，这就像克里斯·迪克森的模式，他总是说你要解决供应过剩的问题，而不是需求不足的问题。现在我们要解决的是供应问题，因为需求非常旺盛，无疑供应问题将会得到解决。
▶ 英文原文 ⏱
So I think that, again, like the sort of just as we talked about the sort of fog of war around margins, I think this question of what is the optimal seed around size and how much capital can you put to work effectively is a much more nuanced topic. I mean, it's sort of this embarrassment of riches, but I'd rather have this problem than the problem we had five years ago, which is, "Hey, my fintech company is indirectly subsidizing their customers through weak underwriting and we don't know the path home." Yeah. Yeah, the Chris Dixon model, which is you always want the problem of supply, not of demand, right? Right now we have to fix the supply part, right? But the demand is like so abundantly there that undoubtedly that will, the supply part will get fixed.
也许我可以用 Mosfa 提出的最后一个问题来结束这次讨论。关于中小企业（SME）采用人工智能的问题。与大型企业不同，中小企业在采用人工智能时的阻力要小得多，因为它们所需的变更管理较少。对此我大致同意，但并不完全赞同，因为有时中小企业会需要更多的习惯改变来适应。不过，问题是，面向中小企业的创业公司在市场策略上有何变化，特别是在人工智能时代？对现有的中小企业来说，我认为他们依然沿用过去的渠道来联系。但是，我认为在人工智能时代，营销一个有趣的变化是，现有的网络都已经习惯于运用建立新网络的方法，他们非常小心，以确保没有人在他们的网络上这样做。
▶ 英文原文 ⏱
Maybe I'll close on this one last question from Mosfa. So double-clicking on the SME sector adoption of AI. So unlike large enterprise, the friction of adoption is much less because they require less change management. I agree with many of that, but not all, small, small medium businesses sometimes have more habit change that you got to work through. But the question is, how do you see the go-to-market playbook for startups targeting SMEs and has that changed in the age of AI? Well, a lot of it for existing SMEs, I think it's the same channels with which you historically reached them. I actually think that one of the interesting things about marketing in the age of AI is that all of the sort of existing networks have been so trained on the methodology of building new networks that they're very careful to ensure no one does it on their networks.
因此，Instagram、TikTok、X这些平台很难在现有平台的基础上创建一种新的分发渠道。因此，创业者需要做的是开发一种能够通过口碑传播的产品，因为这才是最初的网络效应。
▶ 英文原文 ⏱
So Instagram, TikTok, X, it's very hard to build a new sort of distribution channel off the backs of an existing one. So what founders have to do is actually build a product that has the original network effect, which is word of mouth.
我们确实看到，口碑宣传越来越受到重视。是的，以前用于接触中小企业的渠道依然存在。然而，最有趣的市场 segment 是新业务的形成，顺便说一下，现在的新业务成立达到了历史新高。除了 COVID 疫情期间的高峰期外，我认为这是有史以来的最高点。
▶ 英文原文 ⏱
So we're definitely seeing more of a focus on word of mouth. Yes, the kind of old channels for reaching SMEs are still there. Actually, the most interesting segment of the market, though, is sort of new business formation, which is, by the way, at an all-time high. I think it's the highest it's been outside of a peak sort of moment during COVID.
这些人原本不会成为中小企业主。他们不是那种55岁的水管工，而是25岁的年轻人，以前可能是YouTube创作者，现在正在为他们的社区、城市、高中或其他地方开发SaaS软件。
▶ 英文原文 ⏱
These are people who would have never otherwise been SMEs. It's not the sort of 55-year-old plumber. It's a 25-year-old who previously would have been a YouTube creator and now is building SaaS for their, you know, neighborhood or their city or their high school or whatever else it is.
好的，好，太棒了。非常感谢你的聆听。每次有你在真是太好了。我知道你是个时尚达人，我们会在社交媒体上不断分享相关内容，非常感谢你。
▶ 英文原文 ⏱
Yep. Yep, yep. Awesome. Well, thank you so much for listening. It's always great to have you on. Now I know you're a fashionista and we're going to be clipping that endlessly on the socials, but thank you for that.
如果大家有任何问题，你们知道如何联系Dinesh，我们也会在这里跟进一些没有来得及回答的问题。谢谢大家。
▶ 英文原文 ⏱
And if folks have any questions, you know where to find Dinesh, and we'll follow up here for some of the questions we weren't able to get to as well. Thank you.