本集简介
双语字幕
仅展示文本字幕，不包含中文音频；想边听边看，请使用 Bayt 播客 App。
最有趣的新大趋势是个人智能助手。
The most interesting big new trend is in personal agents.
我们现在处于一个对ChatGPT把一切变成对话感到兴奋的世界，现在这些智能助手可以集成到我们的消息应用里，让更多事情变成对话。
We're in this world where we're so excited by what ChatGPT did with turning things into conversation, and now these agents we can get in our messaging apps to turn them into more conversation.
这真是一个不可思议的模式。
It's this incredible paradigm.
大约一半的美国人表示在使用人工智能。
About half of Americans report using AI.
大约4.5%的美国消费者在订阅人工智能产品。
Around 4.5% of US consumers are paying a subscription to an AI product.
最顶尖的1%用户每月在个人信用卡上花费90.03美元用于人工智能。
The top 1% user is spending $90.03 per month personally on their personal credit cards on AI.
现实是，人们现在正在构建软件。
The reality is people are now building software.
他们正在构建这些非常丰富的产品。
They're building these really rich products.
他们可能会用他们的代理来写一部分代码，但当他们向用户交付整个体验时，这远不止是模型上的一个简单包装。
They may be using their agents to write some of that code, but when they're delivering this whole experience to a user, it is much more than just a shim on the model.
OpenAI通过广告达到的运营率，这告诉我们什么？
The run rate that OpenAI has reached through advertising, what does that tell us?
以前，即使推出了广告产品，公司也需要很多年才能达到这个水平。
That would previously take a company years and years and years to get to even after launching an ads product.
关于广告产品的发展，我特别感兴趣的是
What I'm fascinated to see in terms of the evolution of the ads product is
现在有一半的美国人表示在使用AI，但只有一小部分人真正在付费。
Half of Americans now report using AI, but only a small fraction are actually paying for it.
而那些付费的人，花费相当可观。
And the people who do are spending a lot.
在本期节目中，a16z的Elena Burger与合伙人Olivia Moore和Josh Elman一起，深入解读我们第七期消费级AI应用百强报告。
In this episode, a16z's Elena Burger sits down with partners Olivia Moore and Josh Elman to unpack the seventh edition of our top 100 Consumer AI Apps report.
报告首次包含了消费者支出数据，这揭示了一个与仅看流量完全不同的景象。
For the first time, the report includes consumer spending data, revealing a very different picture from traffic alone.
花钱最多的是开发者、创作者和其他重度用户，其中前1%的用户每月在AI工具上的花费超过900美元。
The biggest spenders are developers, creators, and other power users, with the top 1% spending more than $900 a month on AI tools.
他们还谈到了个人代理的兴起，ChatGPT、Claude和Gemini之间的差异，广告如何可能解锁新的消费级AI商业模式，以及那些仍然非常开放的领域，从购物、娱乐到社交和约会。
They also get into the rise of personal agents, how ChatGPT, Claude, and Gemini are diverging, why advertising could unlock new consumer AI business models, and the categories that remain surprisingly wide open, from shopping and entertainment to social and dating.
欢迎收听a16z播客。
Welcome to the a 16 z podcast.
我是Elena Burger，今天和我一起的是Olivia Moore，她是我们持续发布的Top 100消费级AI应用报告的作者，还有我们消费团队的Josh Elman。
I'm Elena Burger, and I'm joined by Olivia Moore, who's the author of our ongoing top a 100 consumer AI apps report, and Josh Elman on our consumer team.
今天，我们将深入探讨这份报告的第七版，谁在为AI付费，为什么各大助手正在走向不同的方向，以及个人代理能否将日常琐事变成下一个大型消费业务。
Today, we're digging into the seventh edition of this report, who's paying for AI, why the big assistants are heading in different directions, and whether personal agents can turn everyday errands into the next big consumer business.
团队，非常感谢你们的参与。
Team, thanks so much for joining.
非常感谢你们的邀请。
Thanks so much for having us.
Olivia，我们直接开始吧。
Olivia, let's just get right into it.
我很想听听你对这份报告的主要看法。
I would love to hear your top takeaways from this report.
这是我们第七次做这个榜单了。
So this is the seventh time we've done this list.
第一次的时候，我们只按流量来排名全球所有AI原生的网站，就是看网站的访问量。
The very first time we only ranked all global websites that were AI native by just traffic, like hits on the website.
那时候这样是可行的，因为当时几乎所有人用AI的方式就是在浏览器里打开一个提示框产品。
And that worked then because the way that almost everyone was using AI was like a prompt box product in a browser.
随着AI的发展，过去几年我们不得不对这个方法进行很多调整。
We've had to evolve that a lot over the last few years as AI expands.
所以这次榜单是我们迄今为止覆盖范围最广的一次。
So this list is our most expansive yet.
我们第一次加入了收入数据。
We added revenue data for the first time.
我们和EBITDA合作，专门获取了消费者的银行卡消费数据。
So we worked with the EBITDA to get specifically consumer card spend data.
所以这不包括企业或中小企业。
So not accounting for enterprises or SMBs.
我们根据消费者实际付费情况对产品进行了排名。
And we ranked products by what consumers are actually paying for.
对我来说，这份报告最大的收获是，从流量角度看，
I think for me, the biggest takeaways from this report is on a traffic basis.
很多产品似乎已经趋于稳定。
It looks like a lot of these products are kind of settling.
在网页和移动端合并列表中，我们只新增了11个产品，这是历届系列中最少的一次。
We only had 11 new products across the web and mobile list combined, which is the least we've had in any of the prior editions of the series.
但消费数据确实引入了很多新变量，因为在消费排名前50的产品中，有29个没有出现在任何一个流量榜单上。
But the spend data really introduced a lot of new variables because of those 50 that ranked for spend, 29 of them were not on either traffic list.
而且我认为这也呼应了报告中的另一个重要结论，那就是当前消费级AI很大程度上是重度用户主导的游戏。
And that also I think kind of works towards or connects into the other big conclusion of the report, which is just how much of a power user game Consumer AI is right now.
这挺有意思的。
That's something that's funny.
我们最近也发布了市场状况报告，莎拉·王直接评论说，嘿。
We also recently released our state of markets and something that Sarah Wang actually commented directly on is she said, hey.
现在确实是一场赢家通吃的游戏。
There there is such a power law game happening now.
这既发生在企业层面，也发生在消费者层面。
It's happening at the enterprise level, and it's happening at the consumer level.
所以有一点可能值得注意，是的，我们这份名单上的新面孔最少，看起来这个领域正在固化。
So one thing that's probably notable is, yeah, we have the fewest number of newcomers on this list, and it seems like the space is ossifying.
但我认为过去几个月，情况实际上有所回升。
But I think the past few months, things have actually been picking up a little bit more.
我觉得最有趣的大趋势是个人代理。
I think the most interesting big new trend is in personal agents.
那你看到了什么？
So what are you seeing?
正在发生哪些重大变化？
What are the big shifts that are happening?
挺有意思的。
It's funny.
我们上一份报告发布的时候，正好在Open Claw流量进来之前就截止了。
The last report that we did, it ended right before the cutoff of when Open Claw traffic would come in.
所以我们在发布报告之前做了一次回顾，发现它本来会排在很靠前的位置。
So we had done kind of like a retro before we published the report and we had found that it would have been very high up in the rankings.
而这次呢，Open Claw在排名里完全不见了，因为流量已经彻底下降了。
And this time actually, Open Claw is nowhere in the rankings because the traffic has completely declined.
我觉得团队是被OpenAI收购了，所以他们可能在做那边的智能助手产品。
I think that the team was acquired by Open AI, so they're probably working on the agent assistant products there.
不过它也被其他一些尝试超越了，比如Grock Bot，Town有一些很棒的智能体，还有OpenAI新出的Dots。
But also it's kind of been overtaken, I think, by some of the both other attempts at prosumer agents like Grock Bot, Town has some fantastic agents, Dots now by Open AI, which is new.
另外在消费端，我们终于看到了一些我觉得可以放心交给家人使用的东西。
But also on the consumer side, we're finally seeing things that I would feel comfortable giving to a family member and telling them that they can safely use.
而且这些大多也是因为，正如你所说，这个消费级助手趋势其实也就是最近三周到一个月才兴起的。
Most of those also, because to your point, this consumer assistant trend has literally been the last three weeks to a month.
其中很多还没有出现在排名里，但我们确实拉了数据，看看它们的情况如何。
A lot of those didn't appear on the ranks yet, but we did pull the data to see what's happening with them.
增长非常非常快，但我觉得还没有真正触达主流用户。
And it's been very, very fast growth and yet still not quite reaching the mainstream users, what I would say.
我觉得我们正处在一个非常有意思的时代，这个时代在去年年底、今年年初因为OpenClaw发生了真正的变化——AI从我们用来提升效率的工具，或者帮我们更快搜索的工具，变成了能真正帮我们搞定事情的东西。
Think we're in this really interesting era that did really change at the end of last year, beginning of this year with OpenClaw, that AI went from something that we were using as a tool to enhance our productivity to maybe search for us faster, something that would actually get things done for us.
我觉得OpenClaw在这方面是了不起的先驱，而且我知道他们还在通过他们的开源基金会继续推进这方面的工作。
And I think OpenClaw was an incredible pioneer to do this, and I know they're still doing a lot of work through their open source foundation to continue to advance that.
但它确实让很多人开始思考：这些产品到底能变成什么样？
But it really turned a lot of heads on, wait, what can these products actually be?
我们看到有人开始买Mac mini。
And we were seeing people buying Mac minis.
我们看到有人去折腾、去破解。
Were seeing people The hack.
用他们自己的本地OpenClaw来跑自己的数据。
Run all their own data with their own local open cloud.
当时确实掀起了一股热潮，但看到这种新的可能性带来的能量，真的很鼓舞人心。
There was a huge run on that, but it was really inspiring to kind of feel this new energy of what is possible.
而且就像大多数趋势一样，火花一旦点燃，
And like most trends, that spark happens.
第一批产品要真正做出来，还需要一段时间。
It takes a little while for those first products to get built.
要让这些产品推向市场、让用户看到，也需要时间积累势头。
It takes a little while for the momentum to then start getting those even released in the market for users to see them.
所以从某种程度上说，这份报告反映的正是我们在硅谷的播客里经常聊的那些内容。
And so in some ways, though, this report is showing, like, that's what we're talking a lot about on our podcast in Silicon Valley.
但它确实很好地展现了更广阔世界里的实际情况。
But this really does a great job representing what's happening in the broader world.
而且我觉得事实就是，AI真的无处不在。
And that I do think the truth is AI really is showing up everywhere.
即使新进入者不多，现有的这些玩家也都在迅速壮大。
Even if there's not a lot of new entrants, all the entrants are all getting so much bigger.
不是。
No.
我觉得最大的事情可能是……
I think probably the biggest things on...
说到纯粹的消费者辅助，最大的进展我觉得是Muse和Instinct。
When we talk about pure consumer assistance, the biggest developments have been Muse and Instinct, I would say.
虽然报告里还列了很多其他很棒的产品，而且，你知道，Poke是很早的先驱，你是先驱。
Although there's many other fantastic products that we kinda list in the report that have also, you know, Poke was a very early pioneer You're pioneer
这个领域的先驱。
of this.
对。
Exactly.
Tomo是……
Tomo is...
它有很大的用户群，而且做得非常好。
It has a very big audience and is doing incredibly well.
Instinct 之前宣布有10万用户，而且每天以10%的速度增长。
Instinct had announced like a 100,000 users growing 10% day over day.
他们还说，前三个星期有40%的用户绑定了信用卡，而且第一个月平均消费超过1000美元。
They'd announced 40% of users connected a credit card in the first three weeks and they were spending over $1,000 on average in their first month.
Muse 很有意思，因为它在很多方面都算是一次非常成功的发布，尤其是在科技圈里。
Muse is interesting because in many ways very successful launch, especially within the tech community.
我记得数字大概是，头十二天左右有50万次下载，25万活跃用户。
I think the number was like 500,000 downloads and 250,000 active users in the first twelve days or so.
但如果你拿它跟 Threads 比，Threads 是 Meta 最近一次比较大手笔的发布，Muse 在吸引普通大众方面还是差远了，那些不在科技行业工作的人。
But if you look at it versus threads, which was another somewhat recent big splashy meta launch, It still pales in comparison in terms of appeal to the average person who doesn't work in tech.
所以你看下载量的话，Muse 还是主要在美国和加拿大。
So if you look at the downloads, Muse is still on The US and Canada.
所以我们只拿了 Threads 在美国和加拿大的下载数据。
So we grabbed the threads US and Canada downloads only.
头二十二天左右，Threads 大概有1600万次下载，而 Muse 还在500万左右。有意思。
In the first twenty two days or so, threads had about 16,000,000 downloads, and Muse is still sitting around 5,000,000 Interesting.
在同一时间段内。
Over that same time period.
分发机制不一样吗？
Is the distribution mechanism different?
我记得Threads是和你的Instagram账户绑定的，分发起来很自然。
Like, I remember with Threads, it was already tied to your Instagram account, and there was kind of this very natural distribution.
他们是不是也在用类似的策略，还是说分发方式完全不同？
Are they trying a similar strategy with Muse or is it just a different distribution strategy too?
我很好奇
I'd be curious
Josh怎么看。
what Josh thinks.
但在我看来，肯定没那么激进。
But from my point of view, it's not as aggressive for sure.
而且部分原因是服务这些用户的成本要高得多。
And also part of that is because there's a much higher cost to serve these users.
他们不想让它发展得太快太大。
They don't want it to get too big too fast.
而且我也觉得Threads当时非常依赖临界质量效应，也就是说，如果没有大量人迅速在上面交流，你就无法真正形成新网络的那种杠杆效应。
And I also think threads very much had a critical mass effect, which is if they didn't get a lot of people on it communicating quickly, you don't actually get to that leverage of a new network starting to form.
因为Muse仍然是一个非常个人化的工具，他们正在推广它，而且他们……
Because Muse is still such a personal tool that they're pushing it, and they're...
即使只是看到这么早期的采用，以及人们拥有这些体验，就已经非常令人兴奋了。
It's very exciting to kinda even see this early just adoption and people having these experiences.
而且，再说一次，如果你在一月份使用OpenCLON，你会有使用Crockbot的绝佳体验。
And, again, if you're using OpenCLON January, you were having incredible experience of using Crockbot.
当它在今年夏初或春季首次推出时，你确实有过这些令人难以置信的体验。
When that first launched earlier this summer spring, you were having these incredible experiences.
现在我们也看到，正如我认为Olivia所说的，我们的妈妈、我们的家人，那些不只是生活在科技和AI下一步发展循环中的人，也开始参与进来了。
And now we're seeing, as I think Olivia said, our moms, our families, people who don't just live on the tech, what's next in AI cycle.
而我认为这正是这个消费产品真正令人兴奋的地方。
And I think that's what's really exciting about this consumer.
我猜下次我们再聊的时候，会看到大规模的采用。
I assume when we do this again next time, we're gonna see massive adoption.
但我觉得它不需要那么用力去推动，就能产生早期的网络效应。
But I think it hasn't needed to push as hard to have that early network effect.
但我也在想，什么能让这些东西长期留下来，那就是网络效应、市场，以及你能和那些代理互动的各种业务。
But then I also think about what's gonna make these things stick around for a long time, and it is gonna be things like network effects and marketplaces and all the businesses you can interact with those agents.
那也会带来真正的变革。
That's gonna be really transformative too.
这是我们讨论消费者代理和助手时经常提到的一点，我觉得它们正在获得构建平台的复合效应，至少从其他应用生态系统的角度来看是这样，这些应用更容易被代理使用。
This is one of the things we talk about a lot when it comes to consumer agents and assistance, which is I think they are getting kind of the compounding effects of building platform or at least it seems like they are in terms of the ecosystem of other apps that are available and more easily usable by the agent.
我们看到这种情况有利有弊。
We've seen that cut both ways.
比如亚马逊说，我们不允许Muse在我们的网站上浏览和购买产品，但Muse已经有数百个其他合作伙伴。
Like Amazon is like, we're not gonna allow Muse to kind of browse and purchase products on our site, but also Muse has already hundreds of other partnerships
比如，你知道，能在Shopify上买东西。
with You different know, to be able to buy things on Shopify.
没错。
Exactly.
是的。
Yes.
我还是觉得，目前这些助手产品都还没有真正实现人与人之间的网络效应。
I still don't feel like any of the assistant products have unlocked person to person network effects.
对我来说，部分原因在于，这个软件基于邮件，比以往任何产品都更深入地了解我们，而且我自己也会跟我的AI说很多非常私密的事情。
Part of this for me is this software knows us more intimately than any other product in the past based on email, but also I find myself telling extremely personal things to my AI.
所以这里就存在一种天然的矛盾：产品对你这个用户越有用，它对你的了解就越多。
And so there's kind of this natural tension between the more useful the product is to you as the consumer, the more it knows about you.
那你真的愿意把这些信息带到和其他人相处的场景中去吗？
And then do you really wanna introduce that into other context with other people?
这就像很多人会分开用ChatGPT的工作账号和个人账号，但还没有人真正解决这个问题。
It's the same thing as many people have a separate ChatGPT work account and personal account, and no one has quite cracked that yet.
确实没有。
No.
而且我觉得，当我们谈论AI消费者时，隐私、安全和保障变得越来越重要。
And I think as we talk about consumers at AI, privacy, safety, security are becoming more and more important.
我的意思是，显然有关于对AI信任的整体趋势，以及这种认为AI对世界有益的信念，这在某种程度上影响了采用率，也影响了围绕这些产品的一些兴奋感。
I mean, there's obviously the overall trends of, you know, trust in AI and and this kind of belief that AI is good for the world that is in some ways, you know, affecting adoption and affecting some of the excitement around these products.
但在个人层面上，如果你不信任你把自己最私密的东西——你的电子邮件、你的生活或你的信用卡——交给的那个东西，而且它可能会做出你意想不到的越轨行为，或者把你意想不到的信息分享给别人，那这将是这些产品真正发展成大规模消费应用的最大阻碍。
But on a very personal level, if you don't trust the thing that you're giving it your most intimate access to, your email or your life or your credit card, and it might do rogue things that you wouldn't expect or share things with other people you wouldn't expect, You know, that's gonna be the biggest impingement on these things really growing as as massive consumer apps.
所以这是一个……
So it's a...
我们正处在一个迷人的时代，大家都在学习这些新规范。
We're in a fascinating time where we're all learning these new norms.
而这些规范，作为人类，我们其实非常擅长。
And these are norms that, like, as people, we're actually very good at it.
我们非常清楚自己愿意分享哪些信息，别人给我们哪些信息时，我们被信任可以传递给他人，或者应该保护并自己保留哪些信息。
We're very good at knowing what information we're comfortable sharing, what information if somebody gives it to us, what information that we're trusted to be able to pass on to somebody or should be protecting and keeping ourselves.
而我们只是突然期望软件也能做到这一点。
And we're just, like, all of a sudden expecting software to do this too.
这确实是一套很有意思的新东西，我们都在慢慢学。
It's a it's a very, you know, interesting new set of things we're learning.
你们俩刚才提到了这个问题的两个有趣的方面。
Well, you you both just brought up, like, two interesting sort of facets of this.
一个是内在的力量，就是所有平台以及它们如何让智能体与它们互动。
One is, like, there's this endogenous force, which is all of the platforms and how they allow agents to interact with them.
然后还有一个更外在的力量，就是我使用这个有多自在，以及……
And then there's more like this exogenous force of like how how comfortable am I using this and and how...
也许我把内在和外在搞混了。
And maybe I mixed up endogenous exogenous.
一个在外面，一个在里面，没错。
One is on the outside and one is on the knew exactly.
一个是平台在做什么，另一个是你对它的感受。
What the platforms are doing and one is how you feel about it.
或者你对智能体的感受。
Or how you feel about the agent.
而且我……
And I...
我很好奇，你觉得哪个会先解决？
I'm curious which which do you think is going to get resolved sooner?
还有，谁……
And and who's like...
这该由谁负责？
Whose onus is that?
就是说，解决这类问题是谁的责任？
Like, whose whose responsibility is resolving those kinds of questions?
我觉得平台能力提升的速度会比消费者真正愿意使用的速度更快，这在AI领域到目前为止一直是这样。
I think what the platforms are capable of is improving faster than true consumer willingness to use them, which we've seen kind of throughout AI so far.
有意思的是，很多人看到那些助手产品创始人发布的数据，说服务一个用户每月要花几百甚至几千美元，他们就会想，这怎么可能？
It's interesting because a lot of people are like seeing these numbers that founders of these assistant products are posting that it costs like hundreds or thousands of dollars a month to serve a user and like, how is that possible?
我之前也困惑过，但我一直在和David Paulan合作，他负责Assistant Benchmark，人非常棒。
And I was stumped as well, but I have been working with David Paulan who runs assistant benchmark who's fantastic.
我们最近和他一起录了一期播客。
We a recent podcast with him.
是的。
Yes.
他做播客做得很好。
He's a great podcast.
基本上，他建了一个网站，追踪了大概170多个智能体。
So basically, what he's done is he's created a website where they track, I think over a 170 agents.
我昨晚查了一下。
I checked it last night.
今天早上可能就有200多个了。
It's probably like 200 something this morning.
然后他们……
And they...
他让用户去使用这些智能体，然后根据在不同任务上的表现来给它们排名。
He has users kind of use them and then rank them on success on different tasks.
但他做的另一件特别有意思的事是，他组织了一些群聊和社区，聚集了这些智能体的早期用户。
But the other really interesting thing that he does is he hosts kind of group chats and communities of early adopters of these agents.
他有一个群，我记得有1500多用户。
So he has a group of, I think it's 1,500 plus users.
他给我们提供了一些有趣的数据，比如大家都在用智能体做什么。
And he pulled some cool data for us, which is like, what are they doing with agents?
他们在讨论用智能体做什么。
What are they talking about doing with agents?
绝大多数情况下，头号用途还是编程和技术自动化，即使是像Muse和Instinct这样面向消费者的助手产品也不例外。
And the vast majority still the number one use case was coding and technical automation even for these consumer assistant products like Muse and Instinct.
所以从这个角度看，成本那么高也就不奇怪了。
And so under that framing, it's like, oh, of course it costs that much.
这就像我们和一些面向更主流消费者的智能体或助手产品交流时，他们看到的成本是每月几十美元，而不是几千美元。
This is like when we talk to some agent or assistant products that are aimed towards the more mainstream consumer, they're seeing costs in the tens of dollars, not the thousands of dollars a month.
所以我觉得这也许说明，要让消费者真正接受这些产品，我们还有很长的路要走。
So I think that is maybe a representation of the fact that we have a very long way to go in terms of consumers getting comfortable with these products.
而且我觉得，你知道，那些一直在编程、一直在用AI的人，它真的是一个非常强大的增强工具，这在很多数据里都能看出来。
And I think, you know, I think people who've been coding and have been working with AI, I mean, it's been such an incredible enhancer, that shows up in a lot of the data.
你看，在这前100名里，简直不可思议。
You know, on the on the top 100 here, like, it's incredible.
然后我们已经……
And then we've...
那些能做到这一点的人，对生活中的其他事情也有了很多好点子。
People who've been able to do that have had these great ideas for all the other things in their life.
但当你把这个东西交给一个新手，它就像个空盒子。
But when you just hand this to somebody new, it feels like a blank box.
感觉就像，我该拿它做什么？
And it feels like, what should I do with this?
也许有一些例子，比如订餐厅或者订机票，但如果你现在没有这个需求，就很难知道怎么把它个性化。
And maybe there's examples of getting a reservation or booking a flight, but if you're not actively seeking a reservation or booking a flight, it's hard to figure out how to personalize it.
而且我们人类很多时候是向别人学习的，我们得到这些好点子，然后尝试别人告诉我们的东西。
And so much of what we do as people is we learn from others, and we get these great ideas, and we try the other things people tell us.
社交媒体之所以能火，很大程度上是因为人们把东西发上去，别人看到了，也跟着做，然后就产生了连锁反应。
So much of how social media has worked is people put things on social media, and other people see that, and they try the same thing, and then it kinda spawns these effects.
所以我试着……
So I try...
他们在此基础上稍作改动，这些改动就像滚雪球一样越滚越大，伟大的想法就是这样蓬勃发展的。
They make a little riff on it, and then those riffs become, you know, cascading snowballs, and that's how great ideas flourish.
而AI因为太个性化，我们还没看到这种蓬勃发展的景象，除了编程和效率提升这些领域，还有所有创意工作。
And with AI, because so much of it's so personal, we haven't even seen any of that flourishing yet, you know, outside of areas like coding and productivity, you know, all of the creative work.
你看，我做出了这个超棒的视频，你是怎么做到的？
You know, I I created this incredible video, and how'd you do that?
我用AI做的。
I use AI.
你知道，现在有很多工具都能做到这一点，这些工具一直在发展，而且已经存在了。
You know, and there's so many tools that do that that that are, you know, been growing and are here.
但有趣的是，我们现在要思考如何让AI普及到大众，不是那些已经使用、付费的几百万用户，而是几亿、几十亿的潜在用户。
But it's so interesting that, you know, now we have to figure out how to mainstream this for not the, you know, millions and millions of people who are using these and paying and and everything, but for the, you know, tens, hundreds, and, you know, hundreds and hundreds of millions.
如果
If
你去看现在的广泛使用数据，大约一半的美国人表示在使用AI，而大约25%的美国人认为他们几乎每天都在与AI互动。
you look at broad usage data now, about half of Americans report using AI, and about 25% of Americans think they are interacting with AI probably on close to a daily basis.
但消费数据要集中得多。
But the spending data is much more concentrated.
所以这取决于数据来源。
So it depends on the source.
我们的EIPET小组显示，可能低于5%，大约4.5%的美国消费者在订阅AI产品。
Our EIPET panel shows probably sub 5%, around four and a half percent of US consumers are paying a subscription to an AI product.
其他一些来源说低至2%或2.5%，或者可能稍微高一点。
Some other sources say as low as two or two and a half or maybe a little bit higher.
这个数字在增长。
It is expanding.
所以这比一年前翻了一番。
So that's like doubled from a year ago.
但消费仍然非常集中。
But that spend is still extremely concentrated.
即使在付费的那4.5%的人中，前10%的人贡献了超过一半的收入。
So even within that top 4.5% of people who do spend, the top 10% of those are like more than half of the revenue.
前1%的人贡献了20%的收入，而后50%的人只贡献了大约16%。
And the top 1% are 20% of the revenue whereas the bottom 50% are like 16%.
可以说，前10%的用户在推动这个市场。
Like the top 10% of users are driving this market.
关于收入数据，另一个有趣的点是，我们几乎可以逐个人地分析，看看这些在AI上花钱的人到底是什么样的人。
This was the other interesting thing about revenue data is we were able to look at it kind of like on a almost person by person basis, what it looks like, who are these people who are spending on AI.
前1%的用户每月在个人信用卡上花费903美元用于AI。
The top 1% user is spending $903 per month personally on their personal credit cards on AI.
哇。
Wow.
而中位数用户每月花费25美元。
And the median is spending $25.
而且，这些消费都只发生在那4.5%有实际支出的人群里。
And again, all of this is within the only the 4.5% that are spending at all.
Yibbit的数据对我们也很有帮助，让我们能看清他们具体在哪些产品上花钱，而且是个人层面的消费。
Yibbit also was helpful for us because we could see what products are they actually spending on, like on a on on a personal basis.
不出所料，大家主要花在开发者工具、效率工具以及创意工具上，也就是那些用来构建、制作和销售的东西。
And probably unsurprisingly, it was mostly developer tools, productivity tools, creative tools, things used to like build, make, sell.
像N8N、Granola、Higgs Field和Manus这类产品，在高消费群体中的占比远远超出平均水平。
Things like N8N, granola, Higgs Field, Manus, those all were way overrepresented amongst the top spenders.
我的意思是，在某种程度上
I mean, in a
其实挺酷的。
way, is actually really cool.
我的意思是，虽然目前还没普及到所有人，但我们正在开启一代全新的创作者。这些人不只是把AI用于工作，而是真的愿意掏自己的信用卡去写以前没写过的代码，去创作艺术、制作视频和内容，真正体验到那种‘创造’的力量。
I mean, even though these numbers aren't everybody yet, like, we're enabling this whole new generation of makers of people who are, you know, not just using this for work, but actually using their personal credit cards to code things they they never coded before, to make art, make videos, make content, and, you know, really have this power of making.
所以，尽管现在还处于早期阶段，但能看到大家因为对创作充满热情而愿意花钱，这一点其实真的很酷。
And so it's actually, in some ways, while it's all still so early, it's actually really cool how much making is going on that people are actually so excited they're willing to spend.
我的意思是，如果你每个月花900美元，是的。
Mean, if you're spending $900 a month Yeah.
在一套AI工具上，你真的是在用它来，嗯，希望让事情更快、更好，让脑子里更多的想法变成现实。
On a set of AI tools, you're really using that to, like, hopefully make things faster, better, have more ideas in your head coming to life than ever before.
所以这有点……
So that's kind of a...
当我看到创造力和编程之间的这些类别时，你知道，这实际上希望真的、真的很有力量。
When I when I was looking at these categories between creativity and coding, like, you know, that's actually hopefully really, really powerful.
嗯，我觉得这一点非常重要，那就是我认为开发者和程序员天生就会问，我怎么能利用这个东西来获得杠杆？
Well, I I I think this is a point that's really important, which is I think developers and coders are people who are naturally attuned to sort of ask, how do I get leverage over this thing?
我怎么能构建一个产品，让我能利用我的时间？
How do I build a product that allows me to get leverage over my time?
而AI显然大大加速了这一点。
And AI obviously supercharges that.
但这……
But it's...
我觉得这是一种特定的思维方式，也是一种特定的世界观。
It is a specific kind of mindset, I think, and it's a specific kind of worldview.
而且我觉得，从这些数字来看，这种世界观仍然只体现在一小部分人身上。
And I think part of part of my understanding with these numbers is it's still a worldview that gets reflected only among a small...
更小的一部分人。
Smaller group of people.
那么问题可能就是，怎么去扩大这种世界观？
And the question might be just like, how do you expand that worldview?
怎么帮助人们理解，有哪些方法可以让你更好地掌控自己的时间和生活？
How do you help people understand like here are all the ways you can get leverage over your time and over your life?
我们在报告里稍微讨论过这一点，就是关于哪些领域还没有出现AI原生的初创公司，或者哪些类别还没有。
We talked about this a little bit in this report in terms of areas where we have not yet seen AI native startups pop up or categories where we haven't.
我们投资组合中的一位CEO，尤金妮亚，经营着一家叫Wabi的公司，这家公司非常棒。
Eugenia, who's one of our portfolio CEOs, runs a company called Wabi, which is fantastic.
她有一句很棒的话。
She has this great quote.
我可能会说错，但大意是，人们……
I'm probably gonna botch it, but it's something like people...
大多数人并不是想节省时间。
Most people aren't looking to save time.
他们是在寻找如何度过时间的方式。
They're looking for ways to spend their time.
这就是为什么社交媒体、娱乐、Netflix、TikTok，还有所有这些YouTube，都那么……
Like, this is why social media, entertainment, Netflix, TikTok, all of these YouTube are so...
成为我们最常用的消费产品。
Are the most used consumer products that we have.
而且我觉得，到目前为止，我们在AI领域看到的很多，就像你说的，都是怎么把这件事做得更快一点、更简单一点、更出彩一点。
And I think that much of what we've seen in AI thus far to your point is like, how do I do this thing a little bit faster, a little bit easier, a little bit more impressively?
但对大多数人来说，这并不能构成一个特别有吸引力的每日或每小时活跃价值主张。
And that's not an incredibly compelling daily or hourly active value proposition for most people.
对。
No.
我觉得，你知道，节省时间仍然是非常，嗯，高生产力的，那些专注于工作或创作的人。
I think I think this, you know, saving time is still very, you know, hyper productivity, people who are focused on, you know, work or being creative.
但我也看到下一波人，他们可能以前是程序员。
But I'm also seeing this next wave of people who are maybe used to be coders.
他们可能不再是那些想做视频但从未完全掌握Adobe Premiere或其他专业工具的人，他们突然能够注册这些服务，把脑海中的想法变成现实。
They're no longer maybe people who, you know, wanna make videos but never quite mastered the, you know, pro tools in Adobe Premiere or something else, where they're all of a sudden able to sign up for these things and take the ideas in their head and make them happen.
所以我觉得，我们开始看到这些兴起。
So I do think, you know, we're starting to see these rise up.
而且，随着……
And, as...
你知道，我看着这些数字，我们仍然处于非常早期的阶段。
You know, I look at all these numbers as we're still so early.
是的。
Yes.
而且，我们甚至能在这里谈论这些公司有多大，以及有1%的人每月花费900美元，这本身就说明了问题。
And and the fact that we're even here and that we're talking about how, you know, big some of these companies are and that 1% of people are spending $900 a month.
我觉得这些是未来的先兆，随着越来越多人感到自己有能力、觉得自己能创作出来。
Like, I see these as harbingers of what's to come as more people feel empowered, as more people feel like they can create.
它不再仅仅是……
Where it doesn't...
不一定只是为了省时间。
It's not necessarily about saving time.
而是为了享受那种把想法真正变成现实的快乐。
It's about spending the joy of, like, actually willing this thing into reality.
你知道，我们……
You know, we...
之前大家开玩笑说，当所有人都刚开始用 OpenClaw 和他们的智能体时，那会儿大家都背着电脑到处走。
There there were all these jokes of, you know, oh, when people were in this, like, the peak phase of everybody's using their OpenClaw for the first time, their agents, they'd walk around with their computers and their laptops.
他们得一直开着机，还说：‘我都不睡觉了。’
They had to keep them open, and they they were like, I'm not sleeping.
我一直在写代码、做东西，你知道的，比过去任何时候都投入得多。
I'm coding, and I'm making things, you know, more than I ever have.
而且我觉得这开始，你知道，又在数据顶部显现，但我确实觉得它开始到处显现了。对。
And I think that's starting to, you know, again, shows up at the top of the data, but I do think it's starting to show up sort of everywhere Yes.
在这整个列表里。
Across this list.
我其实不一定要看到那4.5%直接为AI产品付费的人扩大。
I actually don't necessarily wanna see that 4.5% of people paying for AI products directly expand
这可能有点争议，因为我投资了消费级AI产品，而且我是个消费级AI的超级拥护者。
Which is maybe controversial because I'm, like, invested in consumer AI products and I'm a consumer AI maximalist.
但我这么说是因为，到目前为止，消费级AI的收入几乎全部来自直接订阅。
But the reason I say that is because substantially all of the Consumer AI revenue thus far has come from direct subscriptions.
比如在这个列表里，我们看了一下，发现我们的网络列表里大约85%是通过订阅变现的，另外62%是通过积分或额外使用量付费变现的。
Like on this list, we looked and it was like, you know, 85% or something of our web list monetized via subscriptions and other 62% monetized via kind of credits or token extra usage payments.
只有大约13%有广告或其他选项，那种你是产品而不是为产品付费的模式。
Only like 13% had ads or other options where like you are the product instead of paying for the product.
这就像...
And that's like a...
如果你回顾一下消费互联网的历史，这种模式其实挺不自然的。
If you look back at the history of the consumer Internet, that's like a pretty unnatural inversion.
我们现在几乎所有大型消费科技公司，大部分收入都来自广告和交易费用，而不是订阅。
Like almost all of the really big consumer technology companies that we have now make the vast majority of their revenue from ads transaction fees, not from subscriptions.
因为我觉得事实是，就像不是每个人都需要一个编程工具来帮助他们创业或者创造和销售产品一样。
Because I think the truth of it is most similar to the fact that not everyone is looking for a coding tool to help them, you know, start a business or create and sell a product.
大多数人没有那么多资金，或者不想把钱花在软件上。
Most people don't have the funds or don't want to spend their funds on software.
如果你看看全球前20的消费订阅产品，ChatGPT已经上榜了，这真的很疯狂，才发展了三年半。
If you look at the top 20 consumer subscription products globally, ChatGPT is already on there, which is crazy in like three and a half years of growth.
其他几乎都是媒体公司或者像亚马逊、甚至优步这样的大平台，这些平台都有非常频繁的购买行为。
Almost all the other ones are media companies or big platforms like Amazon or even Uber, things like that, where you have a very frequent purchase behavior.
所以，我真的很相信我们需要转变。
So So I actually am a big believer that we need to move.
我们已经超越了每个人都需要订阅AI的阶段，我们需要看到其他商业模式重新出现。
We have transcended the need for everyone to buy a subscription to AI, and we need to see these other business models come back.
你知道吗？
You know?
我觉得推理成本下降这件事，我们还处在早期阶段。
I think we're we're still at the early days of inference costs coming down.
人们还在摸索怎么用更低成本的模型来做出好产品，这也还处在早期阶段。
We're still at the early days of people finding ways to run lower cost models to build great products.
随着他们这样做，你知道，互联网上一直存在的那些商业模式，比如交易费和广告，最终会开始奏效，但我们需要那一天的到来。
And as they do that, you know, a lot of the other business models have always been on the Internet from, you know, transaction fees and ads will finally start working, but we we need that to happen.
而我觉得，我们还在那个阶段……
And that's, I think, where we're still...
你知道，有太多东西要学，而且，如果服务成本很高，那至少让我收点钱，让用户付费，这成了早期的默认做法。
You know, there's so much learning and and so so much that, like, well, if it's gonna cost me a lot to serve, at least let me just charge money so that people pay for it, became sort of an early default.
对。
Yes.
我可以说，从过去消费者互联网的很多错误中能看到这一点，但至少对我这个消费者投资者来说，以前如果一家公司在头五年就开始赚钱，那简直让人惊讶，怎么回事？
I mean, can probably speak to this from seeing many prior errors of the consumer Internet, but like it used to be at least for me as a consumer investor, if a company was making money in the first five years, it was like, woah, what's happening?
以前的规则是，先积累用户密度，因为服务他们的成本几乎为零，我们可以等到有流动性和密度后再通过广告或交易收费来赚钱。
The rule was like, let's build density of users and because they cost near zero to serve, we can then wait and make money via ads or start charging for transactions once we have liquidity and density.
但鉴于成本如此之高，情况已经不同了。
And that just isn't the case given how high cogs are.
对。
No.
我觉得这个成本问题确实是个很有意思的挑战，公司会害怕增长太快。
I think I think this cost thing is a is a really interesting challenge, and companies get afraid of growing too fast.
比如，他们实际上……
Like, they actually...
如果增长太快，又没有计费机制，支出可能会失控，很难管理。
If they grow too fast and they don't have meters on how they're charging for it, you can actually have out of control spend that can be very, very hard to manage.
所以，这导致了我们现在看到的前100名应用和流量格局变得非常不同。
So, you know, it's created this this very different shape of what we see in the top apps and the top 100, you know, traffic and everything right now.
但再说一次，这……
But, again, this is...
再说，我们都还处于非常早期的阶段。
Again, we're all still so early.
还有很多东西要建设、学习，要降低成本、提升价值，还要引入广告和其他花钱的方式。
You There's so much to build and learn and bring cost down and bring value up and introduce advertising and introduce other ways to to spend money.
而且，如果我们订阅这些我们喜欢的服务，那才是我们持续获得价值的地方。
And by the way, if if it is, we subscribe to these services we love and that's actually what we get continual value from.
那也很棒。
That's amazing too.
我觉得这是个很好的话题，可以聊聊广告了。
I think this is a a great transition to talk about ads.
你的消费者前100名报告里有一个非常有趣的数据，就是OpenAI通过广告达到的年度经常性收入。
Your consumer top 100 report had a really, really interesting fact in there, which was a run rate that OpenAI has reached through advertising.
他们现在广告的年经常性收入已经达到十亿美元了。
They're at a billion dollars now in in annual run rate on on advertising.
这告诉我们什么呢？
And what does that tell us, you know?
再说，这个数字是八月份的，所以很可能一个月后实际数字还会更高一些。
Again, the number was from August and so there's probably a chance that it's reasonably higher even a month or so later.
不过他们一直在慢慢推出广告，和一系列合作伙伴一起。
But they have been very slowly, I would say, rolling out ads with kind of a network of partners.
而且他们现在已经达到十亿了。
And they're already at, you know, a billion.
十亿美元的年化运行率，以前即使推出了广告产品，公司也要花好多年才能达到这个水平。
A billion in annualized run rate like that would previously take a company years and years and years to get to even after launching an ads product.
我觉得这里有两件有趣的事情。
I think there's two interesting things there.
第一，他们现在有真正的用户密度了。
One, they have real density now.
所以，几天前最后报告的数字是每周活跃用户12亿，这确实是一个真正的密度。
So I think the last reported number a few days ago was 1,200,000,000 weekly active which is like a real density.
第二件让我特别感兴趣的是，随着广告产品的演变，理论上他们应该能收取更高的费用，并且有更高的转化率，因为他们作为用户，对你了解得更多。
And then the second thing and what I'm fascinated to see in terms of the evolution of the ads product is hypothetically, they should be able to charge more and have higher conversion rates because they just know so much more about you as the user.
而且ChatGPT已经推出了像用ChatGPT登录这样的功能，我觉得钱包功能也快来了，诸如此类。
And ChatGPT has launched things like login with ChatGPT, I think a wallet is coming, things like that.
所以你可以想象，这对广告主来说会变得更有吸引力，然后这就会让广告业务更加火爆。
And so you could imagine that it becomes a much more compelling value prop for the advertisers, and then that just, like, juices the ad business even more.
不过我们拭目以待吧。
But we'll see.
现在还为时过早。
It's very early days there.
确实还早，而且我觉得，关于AI里的广告，有一些挺搞笑的讨论。
It's early days, and I think, you know, there have been some really funny conversations about ads in AI.
如果你正在跟一个非常私密的东西对话，然后它突然说，让我给你讲讲健康，让我给你介绍这个神奇的产品，能帮你快速减肥。
If you are talking to this incredibly personal thing, and then it's like, let me tell you about health, and let me introduce you to this, like, crazy product that can help you, you know, lose weight fast.
这可不是在建立对AI的信任。
Like, that's not building trust with AI.
所以我觉得另一个真正有趣的点是，我们作为一个生态系统，该如何……
And so I think the other really interesting thing is, like, how do we as a ecosystem...
这些产品太不一样了。
These products are so different.
它们比以往任何时候都更加个性化。
They're so much more personal than ever before.
它们给出的答案是根据你正在做的事情、你问的问题以及它对你的了解量身定制的。
They're giving you answers that are custom tailored to what you're doing and what you're asking about and what it knows about you.
实际上，引入广告这件事做得非常明确。
They're actually introducing ads has been done very definitely.
我其实印象非常深刻，是的。
I've actually been really impressed Yeah.
对于OpenAI推出的广告。
With the OpenAI rollout of ads.
但我其实从来没见过广告。
But I've actually ever seen an ad.
它标注得非常清楚，而且感觉就像是我正在进行的对话的自然延伸。
It's it's very clearly labeled, it actually feels like a natural add to the conversation that I'm having.
而且我觉得我们也在……
And I think we're also...
你知道，另一个变化是，人们开始更多地依赖ChatGPT来处理更多的查询。
You know, one of the other shifts is people are starting to rely on, you know, ChatGPT a lot more for a lot more queries.
以前可能只是查个事实，或者告诉我这个东西的历史，或者帮我做作业，不一定是解决问题或者我们广告里说的那些事情。
It might have used to have been like, look up a fact or tell me the history of this thing or help me with my homework, not necessarily problem solving and things we're advertising.
但现在人们会用它来寻求真正的建议，或者购物，或者想弄清楚他们到底想去哪里或者想做什么。
But now people are going into it for real advice or shopping or trying to figure out exactly, you know, where they wanna go or what they wanna do.
这些都是很好的机会，当你处于探索模式时，广告往往能很好地帮你解决这些问题。
And those are great opportunities when you're in a discovery mode where ads are actually often a great answer to help you solve that.
所以，更多的商业查询和商业意图也促成了这一点。
And so a lot more of these commercial queries and these commercial intent creates that too.
但是，再说一次，这必须做得非常明确，而不是那种奇怪的、打断性的……
But, again, it has to be done very, very definitely, not this weird, interruptive
我正在体验的非常个性化的体验，广告却被硬塞进对话里。
Very personal experience I'm having that ads just get shoved into the conversation.
我特别好奇广告的单元经济是怎么运作的。
I'd be I'd be super curious to how the unit economics on ads work.
比如说，可能它的成本比较低……
Like, presumably, it's a it's a lower cost quest...
跟编码或者图像生成相比，成本更低一些。
Or lower cost compared to, you know, the coding or or something like that or image gen.
所以对OpenAI来说，他们在广告上能有多少利润率，这真是个很有意思的问题。
So so for OpenAI, you know, what what kinds of margins are they seeing on ads is a super interesting question.
我……
I...
我也很好奇，Josh，你看到的是什么类型的广告？
Curious also, Josh, what what kind of ads do you see?
因为我完全没看到任何广告。
Because I I I don't get any ads.
我想可能是因为我用的是……
I guess I I I guess I use You're
展开剩余字幕（还有 257 条）
用的是专业版。
on a pro plan.
我们是企业版。
It's enterprise.
所以我们没有……
So so we don't...
你知道吗，我最近在做很多旅行研究，而且打算在纽约待几天。
You know, I I was doing a bunch of travel research recently and was was gonna be in New York for a few days.
然后我就开始看到一些关于纽约的活动广告，那些活动我可能想去参加。
And I just started seeing some ads for a couple things that were going on in New York that I might have wanted to do.
我觉得这真的很有用。
And I just found that really useful.
而且，我开始更多地用它来购物了。
And, like, I've started using it more for commercial shopping.
我在找一个特别的钥匙扣，要能方便地拆下代客泊车钥匙，但我在谷歌上其实很难找到这样的东西。
I was looking for a a special key chain that would have a really easy separation for a valet key, and I was having trouble finding this actually on Google.
最后我就想，干脆去问ChatGPT吧，然后我就来回聊，描述我想要的东西，它就开始给我一些很好的例子。
And I finally was like, let me just go ask ChatGPT, and I went back and forth, and I was describing what I wanted and started giving me some really good examples.
最后我就说，太好了。
And at the end, I was like, great.
我还想要一个美国制造的，结果我真的找到了一个很棒的选项，是底特律一家工厂做的，那家工厂经常做很多金属制品和其他东西，给我做了一个特别酷的钥匙。
And I'd also love one that's made in America, and I actually found this incredible, like, option from a factory in Detroit that often makes a lot of other metalwork and other stuff that had this, like, really cool key for me.
而且，这就像那种事情，如果我没有机会真正来回聊，并且实际上
And, like, it was one of those things that if I had never had that chance to really go back and forth and actually
进行那次对话，我就只会看到。
Have that conversation, I just would see.
如果当时有其他广告出现……
And if there had been other ads that I got...
那次对话里没有出现任何广告，但完全开放给广告，它们本来可以帮助我找到东西。
No no ads were presented in that conversation, but it was entirely open for ads that they would have helped me find stuff.
最后，我去了那个网站。
And eventually, went to this website.
我之前从来没听说过那家公司，但这次体验真的很棒。
I had never heard of that company before, and it was a great experience.
真有意思。
So interesting.
我觉得随着他们深入这个领域，OpenAI 可能会在广告精准投放上做到非常厉害的程度。
I feel like we may see as they lean into it, OpenAI being able to do crazy good targeting on...
甚至可能比 Meta 还要强，因为 Meta 之前在这方面算是顶尖的，毕竟它对你了解得特别多。
Like, better than I think Meta, which previously was, like best in class for this just because they knew so much about you.
但你现在是在跟他们进行实时对话。
But like you're having this live conversation with them.
我确实觉得这会帮到 ChatGPT，但同时也会支持很多那些更聚焦、几乎可以说是垂直化的消费级 AI 产品，用企业界的术语来说。
I do think that it'll help ChatGPT but also I think it will also support a lot of these more focused almost verticalized to use the enterprise term consumer AI products.
还有一家在广告上早期就取得成功的公司叫 Open Evidence，它是给医生用的 AI 产品。
So another company that has seen early success in ads is Open Evidence, which is an AI product for doctors.
它已经有 50...
And it already has 50...
我觉得最近的数据是，美国医生中有50%到60%在使用这个产品。
I think the most recent numbers were 50 to 60% density of US...
所有美国医生都在用这个产品。
All of all US physicians on the product.
所以当你拥有这么高密度的、非常有价值的受众，而且这个受众很精准，因为你知道他们是医生。
And so when you have that kind of density of a really valuable audience and it's like a targeted audience because you know, they're physicians.
我觉得广告效果会非常好，而且未来会有更多这样的产品，也应该有更多。
You can, I think, advertise really effectively and there will be and should be more of those, I think?
我们是不是值得退一步，聊聊OpenAI和Anthropic？对。
Is it is it worth talking and taking a step back and talking about OpenAI, Anthropic Yep.
还有Gemini，以及这些……
And Gemini and and sort of these of...
这些公司。
These are companies.
这些实验室从最开始就一直在名单上。
These are labs that have been on the list pretty much since the beginning.
我们有没有看到人们在为这些产品付费的方式上有什么变化？
Are we seeing any kind of evolution in in the way people are paying for these products?
关于这三家产品的不同用户群体，我们能推测出什么？
What can we surmise about the different kinds of users of all three?
我特别特别好奇你看到了什么。
Super super curious what you're what you're seeing.
当然。
Absolutely.
从我们第一次做这个报告开始，主要的结论就是ChatGPT在消费者端无论是使用量还是收入上都是全球主导。
From the very first time we did this report, the main conclusion was like ChatGPT is the dominant global user by both usage and revenue on the consumer side.
这一点现在依然成立。
That continues to be true.
我觉得在ChatGPT之下的所有产品，变化和动荡更多一些。
Everything below ChatGPT has seen, I think, more movements and more shakeups.
所以ChatGPT现在在网络端大约是Claude的六倍，是Gemini的两倍。
So ChatGPT is now ahead of Claude around six x on the web, around two x ahead of Gemini on the web.
未来可能，鉴于很多使用量集中在这些付费的重度用户上，收入数据在这里可能会更有用。
Probably going forward, given a lot of the usage is concentrated in these power users who are paying, the revenue numbers could be even more useful here.
而且，这份报告里让我最惊讶的一点是，我们调查了一组美国消费者，发现Claude在付费订阅用户数量上已经超过了Gemini，这挺疯狂的，因为Gemini的安装基数和整体用户基数都大得多，而且它还有像Google这样的天然分发渠道。
And probably that's one of the biggest surprises to me in this report was we got a panel of US spenders and Claude has actually passed Gemini in terms of number of paid subscribers, which is kind of crazy given Gemini has a much bigger install base and user base overall, and it also has like the natural distribution mechanism of something like a Google.
我觉得这对Anthropic来说当然是个很大的成就。
I think that's a big, you know, accomplishment of course for Anthropic.
我认为这得益于他们在过去几个月里推出的一系列非常成功的产品，比如Claude Design。
I think it's come on the wave of a bunch of very successful products that they've launched over the last few months like Claude Design.
这也得益于他们从二月份开始的国防部相关报道带来的大量媒体关注。
It's also come from a lot more press that they've had starting with the Department of War stuff all the way back in, I think it was February.
另一个主要区别是这些产品的变现方式，而且ChatGPT在变现方面仍然遥遥领先。
The other main difference I would say in how these products are monetizing and again, ChatGPT is still far in the lead by monetization.
他们在美国的付费消费者订阅用户数量大约是Gemini和Claude的三倍。
They have around three times more paid consumer subscribers in The US than both Gemini and Claude.
但Anthropic非常明确地表示过，不做广告。
But Anthropic has said very famously, no ads.
所以他们在订阅方面要激进得多。
So they're much more aggressive about subscription.
他们有大约7.5%的用户订阅了每月100美元以上的最高档套餐。
They have around seven and a half percent of subscribers on the $100 plus per month, like the max plan.
套餐。
Plans.
而ChatGPT和Gemini都只有1%左右。
And that's like 1% for both ChatGPT and Gemini.
所以这些用户自然看起来不一样，即使在付费用户群体中，他们在产品上做的事情也不同。
And so those users naturally look different and are doing different things on the product even in that paid user base.
我们要聊聊创意工具吗？
Should we talk about creative tools?
我觉得，我觉得有一点
And I think I think one
很有意思的是，实际上所有大型实验室在创意工具方面都做得非常出色。
thing that's interesting is actually all of the big labs have gotten really good at creative tools.
而且有一件事挺有意思的，Anthropic 虽然出了名地不碰这个领域，但最后还是做到了，就是做出了图像模型或者视频模型。
And one thing that that actually, like, Anthropy kind of famously abstained from but then somehow achieved anyway was, like, creating an image model or video model.
模型。
Model.
但现在人们都在用代码生成图像和视频，因为代码在创建图像和视频方面表现得非常好。
But now people are making images and videos because coding is so good at creating like images and videos.
所以有趣的是，他们还没有构建图像扩散模型。
So it's interesting that, you know, they didn't build a image diffusion model yet.
但他们仍然能够进行图像生成。
They're still capable of doing image gen.
不过说到纯创意工具，那些在这个领域垂直化的公司，我们看到了什么？
But but about the the pure creative tools, the the companies that have kind of like verticalized in that category, what are we what are we seeing?
创意工具在谁在哪里成功方面分布得非常有趣。
Creative tools is very interestingly distributed in terms of who is succeeding where.
所以有几类创意工具，或者可能用“模态”这个词更合适，这些领域实验室们由于各种原因没有投入精力和关注，这些原因我们可以讨论。
So there's a few categories of creative tools or maybe modalities is the better term where the labs have not focused energy and attention for many reasons we could discuss.
很多都集中在音频方面，比如文字转语音、音乐，Eleven Labs和Suno现在非常稳定地排在我们流量榜的前列，在消费榜上排名也很高。
A lot of those around audio, so like text to speech, music, eleven Labs and Suno are now very reliably near the top of our traffic list and are also ranked very highly in our spend list.
这些实验室有太多事情要做了。
The labs have so many things to do.
他们在开发编程代理。
They're building coding agents.
他们在构建通用人工智能。
They're building AGI.
现在让他们去处理Suno经历过的那些知识产权麻烦，然后推出一个竞争性的音乐模型，这合理吗？
Does it make sense at this point for them to deal with all of the IP headaches that Suno has had to go through and launch a competing music model?
我不知道。
I don't know.
与此同时，Suno已经能够凭借强大的领先优势一路狂奔。
And in the meantime, Suno has been able to kind of run away with a really strong lead.
而且，你知道，我坚信这些实验室会做他们想做的事，而且他们会继续成长。
And, you know, I'm a big believer that, you know, the labs are gonna do what they're gonna do, and they're gonna keep growing.
而且它们显然为所有事情都提供了很好的基础。
And they obviously are great baselines for everything.
但在很多这样的市场中，你会看到非常专业的产品，它们非常清楚自己的受众是谁，以及他们在做什么，能够构建出非常定制化的东西。
But in so many of these markets, you're seeing really specialized products that are really focused knowing who their audience is and what they're doing that's able to build really custom things.
你懂吗？
You know?
如果你想为了好玩或者为了真正制作某样东西而创作音乐，Suno 是一个了不起的工具，从你开始使用它的那一刻起，它就一直与你对话，直到最后。
If you wanna make music just for fun or for something that you're really producing, Suno is an incredible tool, and it speaks to you from the moment you start using it through the end.
如果你需要在你正在制作的产品中加入语音和音频，11 Labs 也很棒。
You know, 11 labs if you need voice and audio within the product that you're making.
它使用起来和互动的方式感觉完全不同。
It just has a way of working and and interacting with it that feels completely different.
当你看看很多视频工具，比如设计工具，甚至像 Figma，它们都设计得非常深入，用于你试图表达自己的东西，它们面向的是设计师，而不是那些想跳过这一步的人。
When you look at a bunch of the video tools, you know, when you look at design tools, even like Figma, like, they're so designed to to use deeply for the thing you're trying to express yourself, and it speaks to designers, not to people who, you know, wanna just skip that step.
我认为我们会从这些真正定制的界面中看到更多的价值，软件层涵盖了你所能做的一切、你保存的东西以及你与它互动的方式，这些才是价值所在，而不仅仅是模型本身。
And I think we're gonna see so much more value from these really bespoke interfaces where the software layer of all the things you can do and the things you save and the way you interact with it become really where the value sits, not just the models.
是的。
Yes.
所以我觉得这是创意工具的第二类，就是图像和视频，它们和音频看起来不太一样。
So this is the second category, I think, of creative tools, which is images and video that looks different from audio.
在图像方面，我觉得就像你说的，OpenAI 在图像上确实下了很大功夫，而且做得挺成功的。
On images, I think to your point, like, OpenAI has really leaned into images quite successfully.
比如图像 2.0 在图像生成和编辑方面都非常非常出色。
Like images two point o is very, very good both for image creation and editing.
然后当然，还有 Nano Banana 和 VO 的各种迭代版本。
And then of course, with the various iterations of Nano Banana and VO.
说实话，我觉得他们确实抢走了很多原本流向那些独立图像生成器的高端消费者流量。
I think they have candidly taken away a lot of the high level consumer traffic that was previously going to these standalone image generators.
比如 Midjourney 在我们最初的排名中相当靠前。
So like mid journey was fairly high on our first ever rankings.
但它现在已经完全跌出流量排名了。
It has fallen off the traffic rankings completely.
但说到重度用户，如果你看收入排名，Midjourney又回来了。
But to your point in power users, if you look at the revenue rankings, mid journey is back.
所以重度用户还是想要那个他们觉得有品味、有审美感，而且能按特定方式调整的模型。
So people, the power users still want the model that they feel has taste, has an aesthetic sense and that they're able to tune in a really specific way.
然后这就引到了视频，对我来说这现在有点像一片蛮荒之地，因为中国公司可以在任何数据上训练，而且确实有真正的优势。
And then that brings us to video which to me is kind of like a wild frontier right now because Chinese companies that can train on any data and do have a real advantage.
视频生成太复杂了，要视觉、声音，还要视觉和声音同步，并且看起来是特定风格，我觉得训练数据越多越好。
Video is just so complex to generate with like the visual, the sound, the visual and the sound synced and looking a specific way that I think like the more data you have to train the better.
他们到目前为止在这方面很成功。
They've been successful there thus far.
我觉得更广泛地看创意工具，我很好奇你们两位有没有遇到过这种情况，我最近一直在让Astra和其他非常强大的模型来生成或创作……
I think more broadly in creative tools, I'd be curious if either of you have experienced this, but I've recently been asking Astra and other really powerful models to generate or create...
帮我生成或编辑创意内容。
Generate or edit creative for me.
它做得非常好，而且它会调用工具去互联网上的其他地方。
And it does a very, very good job and it will go tool call to elsewhere on the Internet.
对。
Yep.
所以我觉得，那些对智能体友好、能让智能体直接使用的创意工具，会越来越有优势。
And so I think that creative tools that are agent friendly and agent accessible are gonna increasingly see really compelling tailwinds.
其实你只要给智能体足够的权限，跟它说，对，你想装什么就装什么，能做的事情真的超乎想象。
It's actually crazy what you can do if you just like empower an agent and just say like, yeah, install whatever you want to install.
我试过……
I had it...
我让Astra和5.5直接对决，帮我做一些动画。
I actually had Astra and five point five go, like, head to head in animating some stuff for me.
然后它就像完全知道该怎么做一样。
And, like, it it was just like it knew exactly what to do.
就像……
It's like...
所以
So
而且我喜欢的一点是，你知道，这些喜欢动手折腾的人，站在最前沿的人，正在摸索出这些能力，而接下来一年我们会做出一些产品，它们会出现在这些榜单上，因为它们会把所有复杂的操作打包起来，让使用变得非常简单。
And and what I love about this is, you know, this is the tinkerers, the people at the edge are figuring out these capabilities, and we're gonna build products over the next year that are gonna show up on these lists because they're gonna take all of this complexity of what you can do and package it up in a way that makes it really easy to use.
而且，就是这样。对。
And, like, that's this Yeah.
你知道，当我想到这个差距，比如所有AI和消费级AI之间的差距，正是这种打包和打造出色产品体验的过程。
You know, when I think of this gap of, like, all AI versus consumer AI, it's exactly this this movement of packaging these up and building great product experiences.
这才能真正把它们变成能够持续下去的东西。
That really turns it into into these things that sustain.
而且我觉得这必须从那些喜欢折腾的人开始，然后他们在X上发帖，然后，你知道，六个月后我们就会看到一个真正的产品或者初创公司从中诞生，或者，你知道，12家不同的初创公司都在竞争。
And I guess it it has to start with the tinkerers and then they post on x and then, you know, six months later we get a real, like, product and start up out of it or, like, you know, 12 different start ups all competing.
在网页窗口之外，已经涌现了大量成熟的产品。
Outside of the web window, there have been a a big proliferation of of mature products.
比如有Granola。
So there's granola.
还有Whisper Flow。
There's Whisper Flow.
还有超级人类。
There's superhuman.
你在那边看到了什么？
What what are you kind of seeing there?
然后，显然，那些存在于聊天和消息中的，比如Instinct。
And then, obviously, the ones that exist in chat and messaging, so in Instinct.
在分发方面你看到了什么，在产品和用户类型方面你又看到了什么？
What are you seeing when it comes to distribution, and what are you seeing just when it comes to the kinds of products and users that they're approaching?
我觉得这里有两个有趣的线索可以展开。
I think there's two maybe interesting threads to pull on here.
一个是，我大概在六到九个月前写了一篇文章，叫《大扩张》，基本上就是在讲，为什么消费公司这么快变成企业公司？
One, I I wrote this piece called maybe six, nine months ago called The Great Expansion, which is basically like, why are consumer companies becoming enterprise companies so quickly?
比如像Crea、Replit、Eleven Labs、Gamma这些。
Like things like Crea, you know, Replit, Eleven Labs, Gamma.
这些产品以前像Canva，花了六年多时间才真正有团队和企业收入。
These products previously like Canva took six plus years to have real team and enterprise revenue.
现在的情况是，它们通过PLG增长非常成功，Whisper Flow和Granola也是很好的例子。
And what's happening is that they're very successful in growing via PLG, Whisper Flow and Granola, another great examples there.
然后因为它们跟工作生产力相关，就被企业拉进去了，创始人就会想，哦，我们得加上隐私、安全和团队方案，但这样分发成本很低，效果也很好。
And then because they are kind of work productivity related, they get pulled into enterprises and then the founders are like, oh, we gotta add privacy and security and team plan, but it's very effective, very low cost distribution.
而且，这对那些工具来说真的很管用，这些工具是……
And again, it works really well for these tools that are...
可以个人使用，但又跟工作沾边。
Can be used personally, but are like work adjacent.
我觉得像Whisper Flow、Granola和Superhuman都是这个趋势的好例子，我们普遍看到，现有企业不愿意或无法蚕食自己现有的界面。
I think things like Whisper Flow and granola and superhuman are another good example of this trend we've seen in general, which is incumbents being unwilling or unable to cannibalize their existing interfaces.
最典型的例子就是谷歌。
The classic example of this is Google.
比如，我们还没看到他们为AI时代重新发明Docs、Gmail或日历。
Like we haven't really seen them reinvent docs or Gmail or calendar for the AI age.
我觉得OpenAI和Anthropic也是这样，他们在现有ChatGPT、Codex、Claude或Claude Code界面之外的产品，成功得少得多。
I think it's even happening with OpenAI and Anthropic where they have been much less successful in anything that is not a launch within their existing ChatGPT or codex or Claude or Claude code interfaces.
所以……
So...
而且我知道他们中的很多人都尝试过孵化这些初创公司的杀手级应用，但我觉得从产品角度来看，他们很难在现有基础上创新，尤其是他们手头还有那么多其他事情要处理。
And I know many of them have, you know, attempted to incubate killers for these many startups, but I think it is difficult from a product perspective for them to innovate beyond that, especially when they have so many other things on their plates.
我觉得你说的都对。
I mean, I think all that's true.
而且我觉得，你知道，我们现在……
And I think, you know, we now...
随着我们习惯了一些界面，我们仍然处在一个巨大变革的时期。
As we get used to some interfaces, we're just still in such a period of incredible reinvention.
比如，过去一年半里我们看到的这么多增长，很大程度上是因为模型变得好多了。
Like, a lot of the reason we've seen so much growth over the past year, year and a half is the models got so much better.
模型变好了，所以那些一直在打造这些令人惊叹的界面的人，这些界面易用、自然、易于分享，能让你的团队快速上手，而且WhisperFlow和Granola就是那么容易上手。
And the models got so much better, so the people who had been spending time creating these incredible interfaces that were easy to use, that were that were natural, that were, you know, easy to share and get a bunch of your team on and, you know, WhisperFlow and granola were just so easy to get started with.
然后你会说，天哪。
You then would say, oh my gosh.
现在，随着模型变得更好，这些产品也变得更好了，所以产品的实用性也大大提升了。
Now they've gotten so much better as the models have gotten better, that the products utility gets so much better too.
不。
No.
所以我们正处于一个令人难以置信的……
And so we're kind of this this incredible...
你打造了一个很棒的产品，但可能它不能完全满足你的需求，因为模型的能力还不到位。
You build something great, but it may not do everything you want because the model capabilities weren't quite there.
然后模型变好了，你的产品需求和使用价值就会爆发，这种上升的浪潮会带动一切。
Then the models get better, your product gets to explode in demand and utility, and this kind of rising tide lifts everything.
所以，认为只有一两家公司能构建这种体验层，我觉得正是这份报告要反驳的观点。
And so to think that, you know, we only have one or two companies that can build that experience layer, I think, is what this sort of report sort of debugs.
当然，那些确实是顶尖的公司，覆盖了这些非常通用的用例，但之所以有这么多其他东西在增长，是因为有太多界面需要被重新发明，而且这种上升的浪潮会带动一切。
Now those are certainly some of the top companies and these very general use cases, but it's, you know, the reason that so many other things are growing is there's so many interfaces that get to be reinvented, and and the sort of rising tide lifts everything.
然后，基础设施的价值可能还是会流向模型提供商，或者流向其他提供推理服务的公司，这又是另一个完全不同的话题了。
And then, you know, the infra value may still go to the model providers or may go to the other things that are serving inference, and there's a whole different conversation there.
但消费者真正接受的，是那些对他们有用、用起来方便、能融入他们生活的东西。
But what consumers are adopting are things that work for them and are easy to to use and bring into their lives.
也许还要再说明一下……
Maybe to also make the...
继续为这里的初创公司辩护。
To continue to make the case for startups here.
OpenAI 和 Anthropic 在很多方面也算是初创公司，但它们太成功了，已经变成了拥有几千人的大公司。
OpenAI and Anthropic in many ways are startups, but they have been so successful that they have become big companies with thousands of people.
而且还有太多额外的考虑……
And there are just so many extra considerate...
就像大船更难操控或掉头一样。
Like a larger ship is harder to kind of steer or to turn around.
所以我觉得，这次我们收入榜单上有一家公司叫 Claude，它是一款 AI 笔记设备，最初在中国和东方起步，现在进入了美国市场。你买这个设备，还可以订阅服务。
And so I think that's like one of the companies on our revenue list this time is Claude, which is an AI notetaker device that started kind of in China and the East and now has come to The US and you buy the device and you can also buy a subscription.
OpenAI 可以也一定会做硬件设备，但就像我们看到的，对他们来说，一切都要花更长时间。
OpenAI can and will do a hardware device, but as we've seen with them, it just everything takes longer.
作为大公司，你可能需要整合其他现有产品。
When you're a big company, you have to integrate with maybe other existing products.
它需要经过更多的制衡和审批流程。
There's more checks and balances it has to go through.
所以我认为大型实验室会继续非常非常成功，而且它们还有很多事情要做。
So I do think that the big labs will continue to be very, very successful and there's a lot more for them to do.
而且我认为初创公司将继续能够找到相当大的机会窗口。
And I think that startups will continue to be able to find these quite big windows of opportunity.
而且
And
这也会……
it will...
听起来，对初创公司来说，问题在于如何打造一个产品，正如乔什所说，随着模型变得更好，产品也会变得更好，但不会被模型进步所吞噬。
It also sounds like the the question for startups is how do you build a product that as you said, Josh, gets better as models get better but doesn't get cannibalized as models get better.
是的。
Yes.
也就是说，你知道，这……
Which is, you know, what it...
这就像是一个很有意思的中间地带。
Like like an interesting kind of sweet spot to exist in.
其实，我几个月前之所以这么兴奋地加入安德森·霍洛维茨，部分原因就是我相信价值现在真的又回到了软件层。
I mean, part of the reason that I was so excited to join Andreessen Horowitz a couple months ago was this belief that the value really now is moving back to the software layer.
既然我们现在有了这些能力超强的模型，而且可以随时使用它们，你就能构建出非常丰富的产品，利用这些模型来实现。
That now that we have these incredible models at our, you know, capabilities and we can access them, you can build really rich products that leverage the models to do it.
你明白吗？
You know?
而且还有新的创新，比如TypeSafe的JED，它甚至让开发者都能用新的方式去构建东西。
And there's new innovations like, you know, JED from TypeSafe that allows even developers to build things and even new ways.
而且，当你拥有这样一个快速、易于集成、能给你代码或非常结构化答案的模型时，我们还没看到所有能实现的惊人新想法。
And we haven't even seen all the incredible new ideas that can be done when you have access to a model like that that's fast, that's easy to integrate, that, you know, gives you code, you know, or or very structured answers back.
正因为如此，现在创造力又回到了体验、你能构建的网络、以及你服务大众并把他们聚集在一起的方式上，利用模型来创造强大的东西，但真正成为资产的是背景和社区。
And so because of that, now the creativity is back to the experience, the network you can build, the ways that you can serve a lot of people and bring them together using the models to be something powerful, but the context and the community really becomes the asset.
这里面有太多的创新空间了。
There's so much in invention.
而且我觉得几乎每个行业都有机会被重新定义。
And I think almost every category has the chance to be reinvented.
你知道，有些大公司能够成功转型，成为他们所在领域在AI时代的重要玩家，但并不是所有公司都能做到。
And, you know, some big companies are gonna be able to make the leap and become the relevant company for their sector in the AI era, but not all of them will.
这时候创业公司就出现了，它们抢先一步，真正打造出最好的全新体验。
And that's where startups come in to kinda beat them to that punch and really be the best new possible experience.
关于这一点，我经常思考个人价值的累积，希望最终能变成多人协作的价值，从而形成对某些产品的粘性。
I think to that point, I think a lot about compounding personal value and hopefully, eventually, compounding multiplayer value that becomes like a lock in to some of these sort of products.
举个例子，比如Town这个产品，它更偏向专业用户的智能助手。
One example I would give is town, which is more on the prosumer end of the agent assistant.
它可以连接你所有的系统，比如Slack、邮件、Google Drive，理解你和你的组织的背景，然后帮你处理事情。
It can connect into all your systems, Slack, email, Google Drive, understand kind of the context of you and your org and then do things for you.
比如，我用它来写很多邮件，还帮我完成了不少这份报告。
Like, I use it to write a lot of my emails, helped with a lot of this report.
而且我知道，人们很容易把这类东西看成是，哦，就是个包装壳，现在这算是稍微不那么贬义的词了，我猜。
And I know it's easy for people to dismiss things like this as like, oh, it's just a harness, which is now the slightly less derogatory term for rapper, I guess.
但Town已经建立了关于我是谁、我说话风格的这些模式，这是其他任何产品都没有的，而且我也不能轻易把这些模式迁移到明天出现的新产品上。
But town has built these playbooks of who I am, what I sound like, that no other product has and that I cannot easily take and migrate over to something that comes along tomorrow.
因为一封99.9%像我写的邮件和85%像我写的邮件之间的区别，就是花十秒钟修改和花十分钟以上修改的区别。
Because the difference between an email that is 99.9% sounds like me and an even an email that's 85% sounds like me is the difference between spending like ten seconds to fix it and like ten plus minutes.
所以我觉得我们会看到更多像Town这样的产品，它们擅长收集上下文并基于这些上下文为你建立模式，这样的产品会继续成功。
And so I think that we'll see more products like Town that do a really good job of collecting context and acting on that context to build playbooks around you continue to be successful.
不。
No.
我觉得完全正确。
I think that's totally right.
我尽量不用像包装壳和封装这样的词。
And I try to avoid using words like harness and wrap.
是的。
Yes.
因为我觉得现实是，人们现在在构建软件，他们在打造这些非常丰富的产品，他们……
Because because I think the reality is people are now building software, and they're building these really rich products, they're...
你知道，他们可能用他们的智能体来写一部分代码，但他们实际上……
You know, they may be using their agents to write some of that code, but they're actually...
当他们把整个体验交付给用户时，这远不止是模型上的一个外壳，或者给模型带点上下文的工具。
When they're delivering this whole experience to a user, it is much more than just a shim on the model or a harness that brings some context to model.
这实际上是一个非常丰富的产品和体验，只不过现在恰好用模型作为实现这一切的一部分。
It's actually a really rich product and an experience that happens to now use a model as part of what makes them able to deliver that.
我觉得这正是让人兴奋的地方，未来会……
And I think that's really the excitement of where that's gonna...
你知道，我们会在这个榜单上看到的。
You know, what we're gonna see on this list.
你知道，很多公司已经在这么做了，我们认为下一波浪潮中，这些公司都会创造更多的价值，而模型只是其中的一部分。
You know, many of these already are doing that, and we think really the next wave, they are all gonna be these companies that have built so much more value, and the model is something that is, you know, just one piece.
奥利维亚，我觉得你提到了你做的这张图表，但也许我们可以把它调出来，看看在Web 2.0和AI之前时代存在的所有产品类别中，还有多少空白空间。
Olivia, I think you referenced this this chart that you made, but maybe we can bring it up is all of the white space that still exists for, you know, just all the product categories that exist in web two and the pre AI age.
那你也许可以聊聊这些类别，以及哪些地方有大的机会。
And maybe you can just talk through some of these categories and where are the big opportunities.
绝大多数普通消费者基本上还是把AI当作搜索产品的替代品，比如谷歌。
The vast majority of pure consumers are basically using AI still as a replacement for search products, things like Google.
尤其是在教育场景中，孩子们用它来写作文。
Maybe especially as you get into education use cases, kids are using it to write essays.
稍微更广泛一点的应用是
A little bit more of an expansive use
作业、健康。
Homework health.
对。
Exactly.
是的。
Yes.
或者职场人士用它来写邮件。
Or people in the workplace write emails.
但如果你看这张图表，它几乎展示了目前我们看到初创公司成功的所有领域，都围绕着‘完成任务’这个主题。
But if you look at this chart, which will show almost all of the kind of areas where we've seen startups succeed thus far have been in and around that theme of getting things done.
比如生产力、照片和视频的设计与编辑、搜索和问答。
So like productivity, photo and video design and editing, search and answers.
很多类别都还是一片空白。
So many categories are wide open.
而且其中很多类别，我觉得可以称之为网络型类别，需要多人协作的产品。
And many of those categories are, I would say, I would call them network categories where you need a multiplayer product.
你知道，约会就是一个例子。
You know, dating is one.
我们还没看到……
We haven't seen...
我们……
We...
当然，那里肯定有初创公司，我希望也相信它们未来会出现在这个名单上，但目前名单上还没有做约会应用的。
There are certainly startups there that I hope and believe will make future additions of the list, but there's no one on the list doing dating now.
招聘也是其中一个。
Recruiting is another one.
我觉得招聘和约会其实在某种程度上是相似的网络，都是试图进行匹配。
I think of dating and recruiting as somewhat similar networks actually in terms of trying to make matches.
社交AI方面，我们确实还没看到什么真正火起来的东西。
Social AI, we really haven't seen anything take off.
大多数社交AI只是人们在现有的社交平台上发布AI生成的内容。
Most of most of social AI is people posting AI generated content on existing social platforms.
然后我觉得还有所有这些其他类别，它们……
And then I think there's all of these other categories that are...
看起来可能更像市场，比如购物、买房、零售之类的。
Look maybe more like marketplaces did like shopping, home buying, you know, retail.
我们得看看这些最终是会存在于像Instinct或Muse这样的代理中，还是作为独立产品存在，或者作为下一批产品——肯定会有很多——或者两者兼有。
We'll have to see if those end up living in agents like Instinct or Muse or living as standalone products or the next ones, which there will be many or living as standalone products or perhaps mix of both.
也许你的代理正在调用AI原生的市场。
Perhaps your agent is calling the AI native marketplace.
所以我对这些事情非常兴奋。
So I'm I'm very excited about those things.
我觉得像游戏和娱乐这类领域，模型还不够好，无法生成普通人喜欢看的内容，可能除了AI微短剧，那些正在火爆起来。
I think also categories like gaming and entertainment, the models just have not been good enough to produce content that the average person likes to watch, which maybe the exception of AI micro dramas, are blowing up.
但我觉得这也很快就会到来。
But I think that is coming soon too.
对。
No.
我觉得你说得完全正确。
I think that's exactly right.
我只是觉得我们现在处于这样一个世界，你知道，我们有点被ChatGPT把事物变成对话的能力所震撼，现在这些代理，我们可以把它们放到我们的消息应用里，让它们变成更多的对话。
I just think we're in this world where, you know, we're kind of, like, so excited by what ChatGPT did with turning things into conversation, and now these agents, we can get in our messaging apps to turn them into more conversation.
这是一个令人难以置信的范式。
And it's it's incredible paradigm.
但当你购物时，你想要视觉上看到选项，去探索，让东西为你定制，并且能够说，我真的很喜欢那个，但我希望领子上有点滚边之类的，然后让AI帮你实现这个想法，甚至可能为你定制出来。
But when you're shopping, you wanna visually see options and explore and have things customized for you and be able to say, I really like that, but I wish it had some piping on the collar or something and actually have AI helped you both realize that and maybe even get it made for you.
而且，购物这方面可以有很多创新。
And, like, shopping can have so much invention there.
娱乐。
Entertainment.
我觉得我们才刚刚开始触及皮毛，是的。
I think we've barely started scratching the surface Yeah.
关于讲更好故事的能力，以及赋能人们成为更好的讲故事的人，从而创造出这些由消费者组成的庞大故事网络。
Of the ability to tell better stories, of the way to empower people to be better storytellers that then creates these incredible networks of people consuming stories.
你知道，TikTok 是个神奇的地方，人们聚在一起，制作小视频。
You know, TikTok was this incredible place where people came together and just create little videos.
你现在能看到世界各地的人，但还有太多想法困在人们脑子里，如果他们能更好地表达出来，那对他们会有很大帮助。
You see people around the world that you're now watching, but there's still so much capturing people's heads that if only they could express their ideas even better, it's gonna help them.
我们将拥有一个不可思议的新网络。
We're gonna have the incredible new network.
所以我觉得我们正处在所有这些领域的风口浪尖，我们需要模型变得更好，这样你才能开始尝试这些想法，并去发明创造。
So I feel like we are just at the cusp of all these categories, and we've needed the models to get better so that you could start to dabble in some of these ideas and invent them.
但随着未来几年这种情况的发生，以及新模型能力的提升，我仍然认为价值最终会落在软件层和产品上。
But as that happens over the coming years and with the new model capabilities, I still think the value is gonna end up in this software layer in the products.
对我来说，这又回到了我们之前讨论的，关于帮助人们节省时间的产品与让人们消磨时间的产品。
To me, this gets back to what we were talking about of products that help people save time versus spend time.
几乎我们在消费级人工智能中看到的所有东西都是节省时间的。
Almost everything we've seen in consumer AI is save time.
而那些消磨时间的产品，往往最终会成为最大的公司之一，甚至可能是最大的。
And that spend time, those end up often actually being amongst the biggest companies, if not the biggest ones.
所以我们也需要这样的产品存在。
And so we need that too to exist.
而且我相信，现在有很多开发者正在努力研发这类产品。
And many, many builders, I'm sure, are tinkering away on that right now.
完全同意。
Absolutely.
完全同意。
Absolutely.
我觉得这是个很好的结尾。
I think that's a great note to end on.
非常感谢两位的参与。
Thank you both so much for joining.
还有，一定要去看看第七版的消费者AI应用百强榜单。
And, absolutely make sure to check out the top 100 consumer AI apps, seventh edition.
谢谢。
Thank you.
是的。
Yes.
谢谢邀请我们。
Thanks for having us.
我们第八版再见。
We will see you for edition eight.
好的。
Yes.
是的。
Yes.
感谢收听本期a16z播客。
Thanks for listening to this episode of the a16z podcast.
如果你喜欢这期节目，记得点赞、评论、订阅，给我们评分或留言，并分享给你的朋友和家人。
If you like this episode, be sure to like, comment, subscribe, leave us a rating or a review, and share it with your friends and family.
更多节目，请前往YouTube、Apple Podcast和Spotify。
For more episodes, go to YouTube, Apple Podcast, and Spotify.
在X和a16z上关注我们，并订阅我们的Substack，地址是a16z.substack.com。
Follow us on x and a sixteen z, and subscribe to our Substack at a16z.substack.com.
再次感谢收听，我们下期见。
Thanks again for listening, and I'll see you in the next episode.
提醒一下，这里的内容仅供参考，不应视为法律、商业、税务或投资建议，也不应用于评估任何投资或证券，且不针对任何a16z基金的投资者或潜在投资者。
As a reminder, the content here is for informational purposes only, should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any a sixteen z fund.
请注意，a16z及其关联公司可能也持有本播客中讨论到的公司的投资。
Please note that a sixteen z and its affiliates may also maintain investments in the companies discussed in this podcast.
更多详情，包括我们的投资链接，请访问a16z.com/disclosures。
For more details, including a link to our investments, please see a 16z.com forward slash disclosures.
关于 Bayt 播客
Bayt 提供中文+原文双语音频和字幕，帮助你打破语言障碍，轻松听懂全球优质播客。